Matthew Robben here, I’m a Program Manager on the Windows Server Performance team and my primary responsibility is Windows Server power management. Server power efficiency is a topic of considerable importance – in today’s difficult economy, IT organizations need to contain and reduce costs. Yet the cost of energy to power and cool a 1U server is now more than the amortized cost of the server (over 3 years). 

Energy efficient hardware and software reduces operational costs and directly impacts an organization’s bottom line. We’re in the midst of developing Windows Server 2008 R2, and one of our goals for the product is to build a server operating system that is more power efficient than all of our previous releases. Furthermore, to help IT administrators better understand server power management and optimize their current Windows Server 2008 installations, we’re releasing a comprehensive white paper called “Power In, Dollars Out: Reducing the Flows in the Data Center” today. The white paper gives detailed explanations of many factors affecting server power efficiency, and contains a list of best practices for optimization.

One of the stated best practices is to properly configure Windows Server 2008’s power management features. According to the Green Grid, just turning on PPM features in the operating system can reduce power consumption by 20%. In Windows Server, this can be done simply by choosing the Balanced or Power Saver power policies (found in the Power Options applet in the Control Panel). Of course, PPM is a complicated technology, with many more toggles than a simple on/off switch. We’ve done quite a bit of work on the Windows Server processor power management (PPM) algorithms and parameters during R2 development. One of the results of this work was the development of a set of parameters that can boost power efficiency by up to 10% on standard benchmark workloads.

Good news - you don’t need to wait until R2 to deploy these new parameters on your servers. This blog post will describe PPM technology, explain the parameters involved, and show benchmark test results for the parameter changes on a commodity server. It will also give you a handy command-line walkthrough of the powercfg.exe commands necessary to implement these changes in your environment.

First, some context. Power management requires cooperation from the hardware and the operating system to work efficiently. For example, hardware might support low power states, but the operating system schedules computational work and is in the best position to decide when low power states can be leveraged. The Advanced Configuration and Power Interface (ACPI) defines an interface between the operating system and server hardware to be used for power management purposes.

ACPI Processor Performance States

The processor has traditionally consumed the most power in a server, which makes it a great candidate for power-efficiency optimizations. To add detail and flexibility for processor power management, ACPI defines a few sets of states for processors. Performance states, or P-states, are one such state that can be leveraged to increase power efficiency.

P-States

Processors can transition between multiple performance states, or P-states. P-states define incremental levels of processor performance, from P0 (most performant) to Pn (least performant). The ACPI specification does not specify a maximum number of P-states, so Pn is used to refer to the highest numbered, lowest performant P-state that a processor supports.

Each successively higher numbered P-state consumes less power than the previous P‑state. Processors can dynamically switch between these states during operation to provide only as much computational capacity as is necessary, which saves power during periods of low usage.

Figure 1 below shows a hypothetical set of six P-states that would be available to a processor. Note that the maximum P-state (P0) has the highest frequency, while successively higher numbered P-states reduce in frequency. In this case, the minimum P-state is P5, so the terms Pn and P5 would be interchangeable.

Figure 1. Illustration of P-state number and corresponding frequency

Tuning P-State Parameters for Increased Power Efficiency

Windows Server contains a number of configurable P-state parameters. These can be used to finely tune the power/performance balance of Windows Server PPM. The defaults for these parameters are tuned to deliver excellent power efficiency for most systems and workloads out of the box. However, these are “safe” defaults. They balance performance and power efficiency. Default settings are shown in Table 1. Note that “P-state increase” in this context refers to a transition to a lower numbered, more performant P-state, whereas “P-state decrease” refers to a transition to a higher numbered, less performant P-state. Looking back to Figure 1, an increase would mean moving upward in the chart while a decrease would mean moving downward.

Table 1. Default P-State Parameter Settings in Windows Server 2008

¹The utilization percentage referenced here is not the same as the CPU usage counter in the Task Manager tool. Without going into more details, this setting is best optimized through empirical experimentation.

²A “state domain” is a dependency between different processor cores or packages on a server.  Often, processor designs require that if one core is at a particular performance or idle state, the other cores or packages in the domain must also be at the same state. The hardware notifies the operating system of this dependency by establishing a domain through the ACPI interface.

During Windows Server 2008 R2 development, our team determined a set of parameters that can boost energy efficiency with a very minor performance cost. Notice in Table 1 that the decrease time default is larger than the increase time default. This setting favors P-state increases over decreases. The default increase and decrease percentage settings of 30 and 50 percent, the default domain accounting policy, and the increase and decrease policy defaults favor P‑state increases as well.

To tune the machine for more aggressive power savings, we suggest reducing the decrease time to 100 ms to match the increase time, changing the increase and decrease policies to favor P-state decrease, and switching the domain accounting policy to 0 (off). We left the increase and decrease percentages as their defaults to ensure that the system PPM parameters were not completely biased toward power savings and to reduce negative performance consequences. Table 2 summarizes these changes.

Important:  Modifying any of these parameters changes the behavior of performance state handling from the out-of-box experience. Before you deploy to production servers, validate the effects of any changes in a test environment.

Table 2. Default and New PPM Parameter Values

These parameters can only be set using the powercfg.exe command-line tool, which is installed by default to the Windows\System32 folder on Windows Server 2008. The commands to change the P-state settings by using powercfg.exe are given at the end of this post.

Energy Efficiency Analysis of P-State Settings

To test the efficiency of these new power settings (henceforth called “Aggressive” settings), we performed a set of benchmark runs on a four-socket quad-core server. Table 3 gives the system configuration.

Table 3. Four-Socket Quad-Core Server Configuration

We ran the SPECPower benchmark with both the default settings and the Aggressive power saving settings. Figure 2 and Figure 3 show the power usage and power efficiency across different workload levels. The Aggressive settings exhibit significant power efficiency over the default settings at a majority of the load levels. The maximum power saving is achieved at 60‑percent workload level on this configuration with approximately 10‑percent improvement in power efficiency when it is compared to the default setting. There is a negligible reduction in overall throughput at utilization levels above 97%.

Figure 2.  System power across varying SPECPower load levels

Figure 3.  System power efficiency across varying SPECPower load levels

These settings were tested on commodity servers with the SPECPower workload. Your particular hardware and workload might deliver different results. Please test any parameter changes before deploying in your production environment.

Changing P-State Parameters with Powercfg.exe

If you decide you want to deploy the new P-state parameter settings in your environment, you’ll first need to verify that your Windows Server 2008 installation is configured to use the Balanced power policy. Verify this by going to Power Options in the Control Panel.

Done? Next, you need to start a command prompt with administrator privileges. Get the binary dataset that represents the current power setting settings for P‑states with the following command line:

>powercfg /getpossiblevalue sub_processor procperf 1

You should see the following:

Type: BINARY

Value: 640864000000A0860100E09304001E00000032000000

This value represents an encoded dataset of power policy parameters. The parameter values for this dataset can be shown with the decode command:

>powercfg /ppmperf /decode 640864000000A0860100E09304001E00000032000000

Verify that your power parameter values match the defaults shown below and in Table 1. If your parameter settings do not match these values, your Windows Server parameters may have already been reconfigured for optimal power efficiency in your environment.

Busy Adjust Threshold: 100

Time Check: 100

Increase Time: 100000

Decrease Time: 300000

Increase Percent: 30

Decrease Percent: 50

Domain Accounting Policy: 0

Increase Policy: 0

Decrease Policy: 1

Next, you need to change the parameter values to match the “Aggressive” settings described in this post. To do so, use the following command:

>powercfg /ppmperf /encode base 640864000000A0860100E09304001E00000032000000 /decreasetime 100000 /domainaccountingpolicy 1 /increasepolicy 1 /decreasepolicy 0

After executing this command, powercfg will print out a binary dataset representing the new values, like the one shown below.

640364000000A0860100A08601001E00000032000000

You need to apply the new dataset by using the setpossiblevalue command:

>powercfg /setpossiblevalue /sub_processor /procperf 2 binary 640364000000A0860100A08601001E00000032000000

Finally, use the setactive command to enable the new parameter set. No reboot is necessary for these parameters to take effect.

>powercfg /setactive scheme_balanced

In the past few weeks, there have been a number of new feature announcements around Windows 7 and Windows Server 2008 R2 at PDC and WinHEC conferences.  From a Server perspective, Power Management, Virtualization, and Greater than 64 processor support are considered the top three features for Windows Server 2008 R2.  I will focus on the greater than 64 processor support, given it is a new milestone for Windows, and sets up the stage for competing on much larger and higher end servers.  The support enables large scale database customers to deploy their solutions on Windows and expect good scalability numbers.  Of course, the scalability realized is highly dependent on the applications and drivers being able to scale well beyond 64 processors.  To do so, application and driver developers are strongly encouraged to read up on the greater than 64 processor work here to see what has changed, and what type of code modifications are necessary to take full advantage of this new capability.  The document describes the architecture, terminology, and goes into more details about the new APIs.

Ahmed Talat

Performance Manager

Windows Server Performance Team

My name is Tim Litton, I work as a Program Manager within the Microsoft Windows Server team, and my particular area of focus is performance optimization for Hyper-V.

With the recent release of Hyper-V, customers are starting to ask us how to configure Hyper-V to get the best performance.  It’s generally recognized that there is overheard running a virtualized environment, but the question that really needs to be answered is how much?

With this in mind, I thought I’d share some of our recent testing of Hyper-V and how disk workloads perform when using Fixed or Dynamic VHDs.  The goal here is to provide some data that backs up the tuning guidance that can be here: http://www.microsoft.com/whdc/system/sysperf/Perf_tun_srv.mspx.

The following graph shows the relative performances for a number of different scenarios (with Dynamic VHD being the baseline).

Fixed VHD always performs better than a Dynamic VHD in most scenarios by roughly 10% to 15% with the exception of 4k writes, where Fixed VHD performs significantly better.

We ran 16 virtual machines when performing these tests (see “How We Tested” below) with the goal of evaluating how well Hyper-V performed in the server consolidation scenario.  Being able to consolidate a number of physical machines onto a single machine and have the virtual machines able to handle the load is a very important design goal for Hyper-V.

The exact result that a customer is going to see will depend on quite a few variables (e.g. how large and often the reads and writes are, how many outstanding I/O there can be at one time), so performing real-world testing is the best way to assess what impact virtualization will have.

Recently, QLogic  published a benchmark for I/O throughput for storage devices going through Windows Server 2008 Hyper-V (http://www.qlogic.com/promos/products/hyper-v.aspx) that closely matches the native performance, thus demonstrating Hyper-V’s ability to bring the advantages of virtualization to large-scale datacenters.

How We Tested

Hardware: DP DL580 G5, 16 x 2.4 GHz (Intel E7340), 16 GB RAM

Disk: HP P800, 25 spindles

Virtual Machine Setup: 16 Virtual machines, each running Windows Server 2008 Enterprise Edition (64 bit), 1 CPU, 796 MB RAM

Testing Software: We used IO Meter (http://www.iometer.org) to generate the workload for the I/O system, with a maximum number of 8 outstanding I/Os per virtual machine to a 100MB file.

On Windows Server 2008 and later, applications can programmatically get information about how the underlying hardware components relate to one another.  Examples include spatial locality and memory latency.  This article describes how developers can get the system topology information and use it to build scalable solutions on multi-processor and NUMA (Non-Uniform Memory Architecture) systems.

To start things off, the following is a refresher of some definitions that will be used throughout the article:

From an application’s perspective, a physical system is composed of three components, Processors, Memory, and I/O devices.  These components will be arranged into one or more nodes interconnected by an unknown mechanism.  The following figure is one type of configuration:

In this example, each node contains four processors shown by squares:   Node X contains processors X₀, X₁, X₂, and X₃.  Other systems may have a different number of processors per node, which may be multiple packages or multiple cores on the same physical package.  Attached to each node is a certain amount of memory and I/O devices.  An application can programmatically identify where the different pieces of hardware are, how they relate to one another, and then partition its I/O processing and storage  to achieve optimum scalability and performance.

Application Knowledge of Processors

Applications can determine the physical relations of processors in a system by calling GetLogicalProcessorInformation with a sufficiently large buffer that will return the requested information in an array of SYSTEM_LOGICAL_PROCESSOR_INFORMATION structures.  Each entry in the array describes a collection of processors denoted by the affinity mask and the type of relation this collection holds to each other.  The following table outlines the type of possible relations:

An application may be only interested in which processors belong to a specific NUMA node so it can schedule work on these processors and improve performance by keeping state in that NUMA node’s memory.  The application may also want to avoid scheduling work on processors that belong on the same core (e.g. Hyper-Threading) to avoid resource contention..

Application Knowledge of Memory

An application may also be interested in knowing how much memory is available on a specific NUMA node before deciding to make allocations.  Making a call to GetNumaAvailableMemoryNode will provide this kind of information.  An example might be an application interested in keeping data its threads are working on (e.g. some sort of a software cache) in memory that belongs to the same node hosting the processors to which the threads have their affinities set.  This way, when the data is not resident in the processor’s cache, the cost of reading and writing to the data in local memory is less expensive than accessing remote memory from another node.  When it is time to make the allocation, Windows provides the VirtualAllocExNuma API that takes in a preferred node number as parameter for determining in which node the application would like the memory to reside.  This is an example of an application choosing to allocate memory from a specific node.

Application Knowledge of Devices

Every driver loaded on the system has an associated interface that it supports and registers when the driver starts.  Storage and networking drivers are common examples.

By calling SetupDiGetClassDevs, an application sets up a list of devices supporting a particular interface and calls SetupDiEnumDeviceInfo to enumerate through the list and get a specific device entry.  Once an application knows which devices it is interested in, it can then use the device properties to identify how a particular device relates to other components in the system like processors and memory. 

1)      Call SetupDiGetDeviceRegistryProperty requesting DEVPKEY_Numa_Proximity_Domain.

2)      If a device does not have this property, then move to that device’s parent.  Repeat until either a proximity domain is found or the root device has been reached.

3)      If no proximity domain information is found , then it is possible the device locality information is not exposed.

4)      Given a proximity domain, applications can figure out which NUMA node a device belongs to by calling GetNumaProximityNode

Tom Hawthorn, Karthik Mahesh - Windows Server Performance Team

A significant percentage of web sites utilize PHP as a platform for dynamic content.  During the development of Windows 2008, Microsoft included improvements that enable PHP to run more efficiently than previous Windows releases.  This article describes how to tune Windows 2008, IIS 7.0 and PHP for environments with a single site and high concurrent user traffic.

Introduction

Windows 2008 and IIS 7.0 have new features and optimizations that allow PHP to run more efficiently and robustly.   The most significant improvement Microsoft made was to update the CGI support in IIS 7.0 to conform to the Fast CGI standard.  Fast CGI saves a process create/destroy operation per request by pooling and reusing multiple worker processes.  This translates into significant performance improvements for PHP applications on Windows.

By default, the Fast CGI support in IIS 7.0 is configured to be conservative when allocating resources in order to best support the scenario where hundreds of web sites are running on a single physical server.  This was thought to be the most important deployment environment for PHP on Windows.  This article describes the differences between tuning a Windows server for a multi-site scenario versus a single site scenario assuming high overall traffic.

Multi-site versus Single-site Web Servers

Let’s talk about two typical environments for web servers where resource management and performance become top concerns: the “web hosting” environment and the “enterprise” environment.

In web hosting environments servers host hundreds of sites on a single physical machine.  There are dozens of companies that sell pre-packaged web sites for under $20 per month.  They achieve cost-efficiency by deploying hundreds of sites per single server machine and they place limits on the amount of traffic that each site can service.  Perhaps it is not much of a surprise that a huge percentage of web sites on the internet run on shared hardware.  Individually, hosted sites are low traffic but the aggregate traffic adds up to some serious load.  Administrators must take care to isolate the sites from each other for security reasons and must ensure that no single site can consume all the resources on the machine.  The default configuration values in Windows 2008/IIS 7.0 are optimized for the web hosting environment.

The enterprise environment is virtually the polar opposite from the hosted environment from the perspective of how software should manage resources.  Instead of strictly limiting and aggressively reclaiming the resources per-site, an administrator wants to give all of the machine’s resources to a single site.  Administrators achieve scale and robustness by load balancing the internet traffic across multiple machines serving the same web site content.  This article describes how to tune Windows 2008 and IIS 7.0 for the enterprise environment.

Request Concurrency

Web requests are made from the user’s web browser to a web server.  The server receives the request, processes it and sends back some data.  An HTTP request is usually small, maybe a few hundred bytes.  However, the response may be large and the ephemeral memory required to generate the response can be even larger.  During the period in which the web page is executing code or waiting for a response from somewhere else (i.e. a database, disk, another web site, etc…) the memory associated with the web request cannot be released.  Once the request is completed usually memory can be released with the exception of cached items.

I refer to the term “request concurrency” to describe the number of requests being processed on a web server at any given moment in time.  As average request concurrency grows on a web server, so does the average memory utilization.  In order to conserve memory a web server can limit request concurrency by queuing new incoming requests rather than servicing it immediately if the number of in-flight requests exceeds some limit.  This approach has the side effect of increasing latency because user requests may need to wait for in-flight requests to complete before they are handled.

Increasing the Default Concurrency Limits

On Windows 2008, an HTTP request will be handled by multiple software component layers beginning with the network stack and travelling up into IIS and then sometimes into third party technologies such as PHP.  Each layer will perform some work on the HTTP request before handing the request on to the next layer.  Each time a layer receives a new HTTP request, it has the option of queuing it or processing it.  Therefore, increasing concurrency limits involves modifying configuration associated with multiple layers.  This section describes configuration parameters in http.sys, IIS 7.0, FastCGI and PHP.

Conclusion

Tuning a Windows 2008 machine for PHP performance in enterprise environments is all about increasing the default concurrency limits.  Remember, if you try out some of the tunings in this article make sure to test the effects of the changes in a controlled environment before deploying them to your front line servers.   Increasing the concurrency limits will generally have the effect of increasing the steady state memory utilization and CPU if concurrency is a bottleneck on your system.  If you don’t have enough memory or your CPU is already fully utilized, don’t increase the concurrency limits!  Finally, the tuned values in this article are values that I found empirically in my own test environment.  They may or may not be the right values for your environment so play around with them to find out what works for you.  Happy tuning!

The third, and final, part of this series covers I/O Completions and Memory Management techniques.  I will go through the different ways to handle I/O completions with some recommendations and optimizations introduced in Vista and later releases of Windows.  I will also cover tradeoffs associated with designing single and multiple process applications.  Finally, I will go through memory fragmentation, heap management, and provide a list of the new and expanded NUMA topology APIs.   

Some common I/O issues

It is recommended to use asynchronous I/O to avoid switching threads and to maximize performance.  Asynchronous I/O is more complex because it needs to determine which of the many outstanding I/Os completed.  Those I/Os may be to different handles, mixed file and network I/O, etc.  There are many different ways to handle I/O completion, not all of which are suitable for high performance (e.g. WaitForMultipleObjects, I/O Completion Routines).  For highest performance, use I/O Completion Ports.  Prior to Vista, scanning the pending overlapped structures was necessary to achieve the highest performance, but the improvements in Vista have made that technique obsolete.  However, it should be noted that an asynchronous write can block when extending a file and there is no asynchronous OpenFile. 

The old DOS SetFilePointer API is an anachronism.  One should specify the file offset in the overlapped structure even for synchronous I/O.  It should never be necessary to resort to the hack of having private file handles for each thread.

Overview of I/O Completion Processing

The processor that receives the hardware interrupt runs the interrupt service routine (ISR).  Interrupts are either distributed across all processors or the interrupts from a specific device can be bound to a particular processor or set of processors.  The ISR queues a DPC (usually to the same processor, otherwise an IPI is required) to perform the part of I/O completion processing that doesn’t need access to the user address space of the process that issued the I/O.  The DPC queues a “Special Kernel APC” to the thread that issued the I/O to copy status and byte count information to the overlapped structure in the user process.  In the case of buffered I/O, the APC also copies data from the kernel buffer to the user buffer.  In the case of I/O Completion Routines (not ports), the “Special Kernel APC” queues a user APC to itself to call the user-specified function.  Moreover, prior to Vista every I/O completion required a context-switch to the thread that issued the I/O in order to run the APC.

These APCs are disruptive because as the number of processors and threads increases, the probability that the APC will preempt some other thread also increases.  This disruption is less likely to happen by fully partitioning the application.  That includes having per processor threads and binding interrupts to specific processors. 

What’s new for I/O in Vista and Above

·         Ability to flag an I/O as low priority.  This reduces the competition between background and foreground tasks, and improves I/O bandwidth utilization.  Low priority IO is exposed via SetPriorityClass PROCESS_MODE_BACKGROUND_BEGIN, also by NtSetInformationProcess(process,ProcessIoPriority,...

·         There are no disruptive APCs running when using I/O Completion Ports.  Also, this can be accomplished for Overlapped structs if the user locks them in memory by using the SetFileIoOverlappedRange call.

·         Ability to retrieve up to ‘n’ I/O completions with a single call to GetQueuedCompletionStatusEx. 

·         The option to skip setting the event in the file handle and skip queuing a dummy completion if the I/O completes in-line (i.e. did not return PENDING status).  These can be done by making a call to SetFileCompletionNotificationModes. 

·         The Dispatcher lock is not taken when a completion is queued to a port and no threads are waiting on that port.  Similarly, no lock gets taken when removing a completion if there are items in the queue when GetQueuedCompletionStatus is called because again the thread does not need to wait for an item to be inserted.  If the call to GetQueuedCompletionStatus was made with zero timeout, then no waiting takes place.  On the other hand, the lock is taken if queuing a completion wakes a waiting thread or if calling GetQueuedCompletionStatus results in a thread waiting.

I/O Completion Port Example

Let’s take an example where the main thread loops on GetQueuedCompletionStatus and calls the service function which was specified when the I/O was issued (passed via an augmented Overlapped structure).  The service functions issue only asynchronous I/O and do not wait, therefore the only wait in the main thread is really on the call made to GetQueuedCompletionStatus.  The following are some examples of “events” whose completion we wait on and suggestions on what to do next once they complete:

-          If the completion is for a new connection establishment, set up a session structure and issue an asynchronous network receive. 

-          If the completion is for a network receive, parse the request to determine the file name and issue a call to TransmitFile API. 

-          If the completion is for a network send, log the request and issue an asynchronous network receive. 

-          If the completion is for a user signal (from PostQueuedCompletionStatus), call the routine specified in the payload.

The timeout parameter on GetQueuedCompletionStatus (GQCS) can cause it to wait forever, return after the specified time, or return immediately.  Completions are queued and processed FIFO, but threads are queued and released LIFO.  That favors the running thread and treats the others as a stack of “filler” threads.  Because in Vista the Completion Ports are integrated with the thread pool and scheduler, when a thread that is associated with a port waits (except on the port) and the active thread limit hasn’t been exceeded, another thread is released from the port to take the place of the one that waited.  When the waiting thread runs again, the active thread count of the port is incremented.  Unfortunately, there is no way to “take back” a thread that is released this way.  If the threads can wait and resume in many places besides GQCS (as is usually the case), it is very common for too many threads to be active.

PostQueuedCompletionStatus allows user signals to be integrated with I/O completion handling which allows for a unified state machine.

Characteristics of I/O Completion Ports

An I/O Completion Port can be associated with many file (or socket) handles, but not the reverse.  The association cannot be changed without closing and reopening the handle.  It is possible to create a port and associate it with a file handle using a single system call, but additional calls are needed to associate a port with multiple handles. 

While you don’t need an event in the Overlapped structure when using Completion Ports because the event is never waited on, if you leave it out, the event in the file handle will be set and that incurs extra locking.

In Vista, applications that use Completion Ports get the performance benefit of eliminating the IO Completion APC without any code changes or even having to recompile.  This is true even if buffered IO is used.  The other way to get the benefit of IO Completion APC elimination (locking the overlapped structure) requires code changes and cannot be used with buffered IO.

Even if the I/O completes in-line (and PENDING is not returned), the I/O completion event is set and a completion is queued to the port unless the SetFileCompletionNotificationModes option is used.

if( !ReadFile(fh,buf,size,&actual,&ioreq)){

    // could be an error or asynchronous I/O successfully queued

    if( GetLastError() == ERROR_IO_PENDING ) {

      // asynchronous I/O queued and did not complete “in-line”

    } else {

      // asynchronous I/O not queued or was serviced in-line and failed

    }

} else {

    // completed in-line, but still must consume completion

    // unless new option specified

}

 Memory Management Issues

When designing an application, developers are often faced with questions like - Should the application be designed with a single process or multiple processes?  Should there be a separate process for each processor or node?  In this section, we try to answer some of these questions while providing the advantages and disadvantages for each approach.

Advantages for designing applications with multiple processes include isolation, reliability and security.  First, an application can take advantage of more than 4GB of physical memory because each process can use up to 2GB for user-mode data.  Second, if memory is corrupted by bad code in one of the processes, the others are unaffected (unless shared memory is corrupted) and the application as a whole does not need to be terminated.  Also, separate address spaces provide isolation that can’t be duplicated with multiple threads in a single process.

Some disadvantages of using multiple processes include higher cost of a context switch compared to a thread switch in the same process due to the TLB getting flushed.  Also there are possible performance bottlenecks introduced by the mechanism chosen for Inter-Process Communication (IPC).  Examples of IPC include RPC, pipes, ALPC, and shared memory, so it is important that the right kind of IPC is chosen. Some estimates for round trip cost to send 100 bytes via RPC: 27,000 cycles, local named pipes: 26,000 cycles, ALPC: 13,000.  IPC via shared memory is the fastest but it erodes the isolation benefit of separate processes because bad code can potentially corrupt data in the shared memory.  Also with shared memory it is not always possible to use the data “in-place” and copying incurs an added cost of 2.5 cycles per byte copied.

Advantages for designing applications with a single process include not needing cross-process communication, cross process locks, etc.  Single process application can also approximate some of the advantages associated with multiple processes via workarounds.  For instance, exception-handing can trap a failing thread making it unnecessary to terminate the entire process.  The 2GB user virtual address limit is gone on x64 and can be worked around to some degree on 32bits using Address Windowing Extension (AWE) or the 3GB switch to change the user/kernel split of the 4GB virtual address space from 2:2 to 3:1. 

Shared Memory Issues

Shared memory is the fastest IPC but sacrifices some of the isolation that was the justification for using separate processes.  Shared memory is secure to outsiders once set up, but the mechanism by which the multiple processes gain access to it has some vulnerability.  Either a name must be used that is known to all the processes (which is susceptible to “name squatting”) or else a handle must be passed to the processes that didn’t create the shared memory (using some other IPC mechanism). 

Managing updates to shared memory:

1. Data is read-write to all processes, use cross-process lock to guard data or lock-free structures. 

2. Data is read-only to all but 1 process which does all updates (w/o allowing readers to see inconsistencies)

3. Same as 2 but kernel does updates

4. Data is read-only, unprotect briefly to update (suffering TLB flush due to page protection change).

Global Data defined in a DLL is normally process-wide but the linker “/SECTION:.MySeg,RWS” option can be used to make it system-wide if that is what is needed.  Just loading the DLL causes it to be set up as opposed to the usual CreateSection/MapViewOfSection APIs.  The downside is that the size is fixed at compile time.

Memory Fragmentation – What is it?

Fragmentation can occur in the Heap or in the process Virtual Address Space.  It is a consequence of the “best fit” memory allocation policy and a usage pattern that mixes large, short-lived allocations with small, long-lived ones.   It leaves a trail of free blocks (each too small to be used) which cannot be coalesced because of the small, long-lived allocations between them.  It cannot happen if all allocations are the same size or if all are freed at the same time.  Avoid fragmentation by not mixing wildly different sizes and lifetimes.  Large allocations and frees should be infrequent and batched.  Consider rolling your own “zone” heap for frequent, small allocations that are freed at the same time (e.g. constructing a parse tree).  Obtain a large block of memory and claim space in it using InterlockedExchangeAdd (to avoid locking).  If the zone is per-thread, there is no need for even the interlocked instruction.  Use the Low Fragmentation Heap whenever possible.  It is NUMA-aware and lock-free in most cases.  It replaces the heap look-asides and covers up to 16KB allocations.  It combines the management of free and uncommitted space to eliminate linear scans.  It is enabled automatically in Vista or by calling HeapSetInformation on the heap handle.

Best practices when managing the Heap

·         Don’t use GlobalAlloc, LocalAlloc, WalkHeap, ValidateHeap, or LockHeap.  GlobalAlloc and LocalAlloc are old functions which may take an additional lock even if the underlying heap uses lock-free look-asides.  The WalkHeap and ValidateHeap functions disable the heap look-asides.

·         Don’t “shrink to fit” (i.e. allocate a buffer for largest possible message, then realloc to actual size) or “expand to fit” (i.e. allocate typical size, realloc ‘n’ bytes at a time until it fits).  These are fragmentation-producing allocation patterns and realloc often involves copying the data.

·         Don’t use the heap for large buffers (>16KB).  Buffers obtained from the heap are not aligned on a natural boundary.  Use VirtualAlloc for large buffers, but do it infrequently, carve them up yourself and recycle them.

·         New in Vista: dynamic kernel virtual address allocation... no longer need to manually juggle the sizes of the various kernel memory pools when one of them runs out of space (e.g. desktop heap).

·         New in Vista:  Prefetch API - PreFetchCacheLine(adr,options).  The API has a large dependency on the hardware’s support for prefetch.

NUMA Support

APIs to get topology information (depends on hardware for the information):

  GetNumaHighestNodeNumber

  GetNumaProcessorNode (specified processor is on which node)

  GetNumaNodeProcessorMask (which processors are on the specified node)

  GetNumaAvailableMemory (current free memory on specified node)

Use existing affinity APIs to place threads on desired nodes.

Memory is allocated on node where thread is running when the memory is touched for the first time (not at allocation time).  For better control over where memory is allocated, use new “ExNuma” versions of the memory allocation APIs.  The additional parameter specifies node.  It is a preferred, not absolute specification and it is 1-based because 0 signifies no preference.

  VirtualAllocExNuma(..., Node)

  MapViewOfFileExNuma(..., Node)

  CreateFileMappingExNuma(..., Node)

Large pages and the TLB

The Translation Look-aside Buffer (TLB) is a critical resource on machines with large amounts of physical memory.  Server applications often have large blocks of memory which should be treated as a whole by the memory manager (instead of 4KB or 8KB chunks), e.g. a database cache.  Using large pages for those items reduces the number of TLB entries, improves TLB hit ratio, and decreases CPI.  Also, the data item is either all in memory or all out.  

  minsize = GetLargePageMinimum();

  p = VirtualAlloc(null, n*minsize, MEM_LARGE_PAGES, ...);

The guide has been updated with sections on Power and Hyper-V guidelines and best practices.  Check out the updated Tuning Guide and tell us what you think by following the feedback link at the top of the Tuning Guide.  We look forward to hearing from you!

Ahmed Talat
Performance Manager
Windows Server Performance Team

Rick Vicik - Architect, Windows Server Performance Team

The second part of this series covers Data Structures and Locks. I will provide general guidance on which data structures to use under certain circumstances and how to use locks without having a negative impact on performance.  Finally, there will be examples covering common problems/solutions and a simple cookbook detailing functional requirements and recommendations when using data structures and locks.

In order to avoid cache line thrashing and a high rate of lock collisions, the following are suggested guidelines when designing an application:

·         Minimize the need for a large number of locks by partitioning data amongst threads or processors. 

·         Be aware of the hardware cache-line size because accessing different data items that fall on the same cache-line is considered a collision by the hardware. 

·         Use the minimum mechanism necessary in data structures.  For example, don’t use a doubly-linked list to implement a queue unless it is necessary to remove from the middle or scan both ways. 

·         Don’t use a FIFO queue for a free list. 

·         Use lock-free techniques when possible.  For example, claim buffer space with InterlockedAdd and use an S-List for free lists or producer/consumer queues. 

The “ABA” problem

The “ABA” problem occurs when InterlockedCompareExchange is used to implement lock-free data structures because what is really needed is the ability to detect any change, even a set of multiple changes that restores the original value.  If 2 items are removed from an S-List and the first is replaced, just comparing values would not detect that the list has changed (and the local copy of the ‘next’ pointer from the first item on the list is “stale”).  The solution is to add a version# to the comparison so that it fails after any change, even one that restored the old value.  That gets tricky in x64 because the size of a pointer is the same as the maximum size InterlockedCompareExchange.

The “Updating of Shared Data” problem

The primary concern when handling updates to shared data is to be aware of all the ways an item can be reached.  When removing from the middle of a doubly-linked list, an item becomes unreachable when the next and previous pointers of the adjacent items are nulled.  The tricky part is that a thread may have made a local copy of the “next” pointer from the previous item before it was nulled but hasn’t yet accessed that “next” item.  A “lurking reader” must make its presence known to others by using a refcount or by taking per-item locks in a “crabbing” fashion as it traverses the list.  The refcount can’t be on the target item because the lurking reader hasn’t gotten there yet.  It must be on the pointer that was used to get there or else logically apply to the list as a whole.  The “crabbing” technique is usually too expensive in most cases.  It is almost always necessary to have a lock which guards the list.

True Lock free Operations

The simple test for a true “lock-free” operation is whether or not a thread can die anywhere during the operation and not prevent other threads from doing the operation.  It is commonly thought that replacing a pointer with a “keep out” indicator is better than using a lock, but it is really just folding the lock and pointer into the same data item.  If the thread dies after inserting the “keep out” indicator, no other thread can get in.

Guidelines for good locking practices and things to avoid

·         Hash Lookup (cache index or “timer wheel”)

A good general practice is to use an array of synonym list heads with a lock per list (or set of lists), where locks fall on different cache lines.  This design does not increase the number of lock acquires per lookup compared to the single lock implementation while reducing the lock collision rate.  Use a doubly-linked list for synonyms to support removal from the middle (if lookup always precedes removal, a singly-linked list can be used).  If the access pattern is mostly lookups with occasional insert/remove, use a Read/Write lock with a “writer priority” policy to provide fairness.  If the entry is not found, then an exclusive lock is needed to insert it.  If the lock doesn’t support atomic promotion, then must drop, reacquire and rescan.  To avoid memory allocations (and possibly waiting) while holding lock, allocate the new block between the dropping and re-acquiring of the lock.

·         N:M Relationship

Suppose a company allows a many-to-many relationship between employees and projects.  That requires a set of intersecting lists to represent the relationships and a single lock can probably be used to guard it.  That solution might be sufficient if most access is read-only, but will become a bottleneck if updates are frequent.  A finer-grain solution is to have a lock on each instance of project and employee.  Adding or removing a relationship requires taking the 2 intersecting locks, which is slightly more than the single lock implementation.  A number of optimizations are possible to avoid lock-ordering issues:  Deferred removal of intersection blocks, one-at-a-time insertion & removal, InterlockedCompareExchange for removal.

·         Lock Convoy

FIFO locks guarantee fairness and forward progress at the expense of causing lock convoys.  The term originally meant several threads executing the same part of the code as a group resulting in higher collisions than if they were randomly distributed throughout the code (much like automobiles being grouped into packets by traffic lights).  The particular phenomenon I’m talking about is worse because once it forms the implicit handoff of lock ownership keeps the threads in lock-step.

To illustrate, consider the example where a thread holds a lock and it gets preempted while holding the lock.  The result is all the other threads will pile up on the wait list for that lock.  When the preempted thread (lock owner at this time) gets to run again and releases the lock, it automatically hands ownership of the lock to the first thread on the wait list.  That thread may not run for some time, but the “hold time” clock is ticking.  The previous owner usually requests the lock again before the wait list is cleared out, perpetuating the convoy.

·         Producer/Consumer Implementation

The first thing you should ask yourself when setting up a producer/consumer arrangement is what the gain achieved by handing off the work?  The amount of processing done per hand-off must be significantly greater than the cost of a context switch (~3k cycles direct cost, 10x or more indirect cost, depending on cache impact).  The only legitimate reasons for handing off work to another thread are:  If the isolation of a separate process is needed or if preemption is needed rather than cooperative yielding. 

The following are code snippets for a Producer/Consumer implementation which will be used to point out things to avoid when doing the design.

1.       Don’t wake the consumer while holding the lock it will need.  In our example, the Producer is holding the QueueMutex when it made the SetEvent call.

2.       Don’t make multiple system calls to process each item even when no scheduling events occur (3 system calls in the producer, 3 in the consumer in this case). 

3.       Using the WaitForMultiple(ALL) call on both the QueueMutex and WakeupEvent seems like a clever solution because it avoids the extra context switch and combines the two WaitForSingleObject system calls into a single call.  It really isn’t much better because each time an event in the set is signaled, the waiting thread is awakened via APC to check the status of all events in the set (resulting in just as many context switches).

4.       Amortize the hand-off cost by not waking the consumer until ‘n’ items are queued, but then a timeout is needed to cap latency. 

5.       It is better to integrate background processing into the main state machine.   

6.       The consumer should be lower priority than the main thread so it runs only when the main thread has nothing to do.  Consumer should not cause preemption when delivering the “finished” notification.

7.       In the example above, consider using PostQueueCompletionStatus rather than SetEvent API.  The latter boosts the target thread’s priority by 1 which may cause it to preempt something else.   

8.       Don’t use the Windows GUI message mechanism for producer/consumer queues or inter-process communication.  Use it only for GUI messages. 

Synchronization Resources

Events are strictly FIFO, can be used cross-process by passing the name or handle, and they have no spin option or logic to prevent convoys from forming.  When an event is created, its signaling mode can be set to either auto-reset or manual-reset.  Each signaling mode dictates how APIs like SetEvent and PulseEvent interact with threads.

Ø  The SetEvent call on auto-reset events will allow a single thread to pass through if no others are waiting on the event.  For manual-reset events, the call will allow all threads waiting on the event to pass and “keep the door open” until the event is explicitly reset. 

Ø  The PulseEvent call on auto-reset events will allow a single thread to pass through but only if there is one waiting.  On manual-reset events, the call will allow all threads waiting on the event to pass and “leaves the door closed”.

Note:  The SignalObjectAndWait call allows a thread to signal another and wait without being preempted by the signaled thread.  The SetEvent call typically boosts the priority of the signaled thread by 1, so it is possible for the signaled thread to preempt the signaling thread before it waits.  This pattern is a very common cause of excess context switches. 

Semaphores are also strictly FIFO, can be used cross-process, and they have no spin option and no logic to prevents convoys from forming.  When it is necessary to release a specific number of threads, but not all, using the ReleaseSemaphores call is recommended.

Mutexes exposed to user mode supports recursion and have the current owning thread ID stored in it, whereas the Event and the Semaphore do not.

The ExecutiveResource is a reader/writer lock available in user and kernel mode.  It is FIFO, has no spin or anti-convoy logic, but does have a TryToAcquire option and has anti-priority-inversion logic (attempts to boost the owner(s) if acquire takes over 500millisec).  There are options regarding promotion to exclusive, demotion to shared, and whether readers or writers have priority (but not all options are available in user and kernel versions).

The CriticalSection is the most common lock in user-mode.  It is exclusive, supports recursion, has TryToAcquire and spin options and is convoy-free because it is non-FIFO.  Acquire and Release are very fast and do not enter the kernel if there is no contention.  It cannot be used across processes.  The event is created on first collision and all critical sections in a process are linked together for debugging purposes which requires a process-wide lock on creation and destruction (but there is an option to skip the linking).    

Read/Write locks (Slim Read/Write Lock (SRWLock) in user mode and PushLock in kernel mode)

The new, lighter-weight read/write lock is available in Vista and later releases of Windows.  It is similar to the CriticalSection in that it makes no system call if there are no contentions.  It is non-FIFO and therefore convoy-free.  The data structure is just a single pointer and the initialized state is 0, so they are very cheap to create.  It does not support recursion.  To start using SRWLocks, visit MSDN for a list of the supported APIs. 

ConditionVariables are also new in Vista.  They are synchronization primitives that allow for threads to wait until a specific condition occurs.  They use SRWLocks or CriticalSections and cannot be shared across processes.  To start using ConditionVariables, visit MSDN for a list of the supported APIs. 

Lock-Free APIs (Recommended when possible)

Atomic updates are typically better than acquiring locks, but locking can still happen at the hardware level which causes bus traffic.  It is also possible to have excessive retries (at the hardware or software level).

InterlockedCompareExchange compares a 4 or 8 byte data item with a test value and if equal, atomically replaces it with a specified value.  If the comparison fails, the data item is left unchanged and the old value is returned.  The S-List is constructed using InterlockedCompareExchange.  It atomically modifies the head of a singly-linked list to support push, pop and grab operations.  It uses a version# to prevent the “ABA” problem mentioned earlier in the post.  InterlockedCompareExchange can also be used to construct a true FIFO lock-free queue.  It is basically an S-List with a tail pointer that could be slightly out-of-date.  It is not widely used because it has the limitation that elements used in the list can never be re-used for anything else.  This is often worked-around by using surrogates for the list elements.  A common pool of them can be maintained in the application.  It can grow but never shrink.

InterlockedIncrement, InterlockedDecrement, and InterlockedExchangeAdd are similar, but the new value is derived from the old rather than specified (add or subtract 1, add specified value... use negative to subtract).  These can be better performing than InterlockedCompareExchange because they cannot fail, so retries are eliminated in most cases.  To add variety, InterlockedIncrement and InterlockedDecrement return the new value while InterlockedExchangeAdd returns the old value.

The Locking Cookbook

The following table provides a list of proposed recommendations which shouldn’t negatively impact performance when working towards meeting a specific set of functional requirements.

Table 1:  A locking cookbook

Conclusion

In conclusion, the following are common guidelines for designing applications for high performance while using locks and data structures to ensure data integrity through the different available synchronization mechanisms.

·         Minimize the frequency of lock acquires and hold time.  Don’t wait on objects, do IO, call SetEvent, or an RPC while holding a lock.  Don’t call anything that may allocate memory while holding a lock.  Note that taking a lock while holding a lock (i.e. nesting locks) inflates hold time on the outer lock.

·         Use a Reader/Writer lock if >70% of operations take the lock shared.  It is incorrect to assume that any amount of shared access will be an improvement over an exclusive lock.  Exclusive operations can be delayed by multiple shared operations even if alternating shared/exclusive fairness is implemented. 

·         Break up locks but not so that typical operations need most of them.  This is the tricky trade-off.

·         Taking multiple locks can cause deadlocks.  The typical solution is to always take locks in a pre-defined order, but in practice, different parts of the application may start with a different lock.  Use TryToAcquire first (it helps eliminate deadlocks because you don’t wait).  If that fails drop the lock that is held and re-acquire in the anti-convoy order.  Things can change between the drop and reacquire, so it may be necessary to recheck the data.  Not all locks have try-to-acquire option.  WaitForMultipleObject(All) is another way to solve the deadlock problem without defining a locking order (deadlocks are impossible if all locks are obtained atomically).  The expense of WaitForMultipleObject(All) is the downside.

·         If you find there is a need to use recursion on locks, then it means you don’t know when the lock is held.  The lack of knowledge makes it impossible to minimize the lock hold time because you don’t know when it was held.  This is a common problem with Object-Oriented design

Networking performance has increasingly become one of the most important factors in overall system performance. Many of the factors that affect networking performance fall under the following three categories: Network adapter hardware and driver performance, network stack performance, and the way applications interact with the network stack. We will highlight some of the more important networking adapter properties and advanced features available to yield optimal performance.

Introduction

Networking performance is measured in throughput and response time. It is also important to achieve the optimal performance operating point without over-utilizing the system resources. To reach that optimal performance point, the network adapter vendors have enhanced their hardware capabilities to improve scalability, the amount of time to process data, and built in capabilities to dynamically adjust hardware and software parameters depending on workload characteristics.

Network Adapter Hardware

Over the past decade, networking speeds have multiplied orders of magnitude to keep up with applications that have become network intensive and the load on the host processors increases for networking service routines.  In light of these changes, it has become increasingly important to consider offloading some of the tasks to the hardware and further optimize how the hardware interacts with the software to scale and improve performance.  Some of the features include Task Offload, TCP Offload, Interrupt Moderation, dynamic tuning on the hardware, Jumbo Frames, and Receive Side Scaling (RSS).  These are particularly important for the high-end network adapter that will be used in configurations requiring top performance.

Task Offload Features

The Microsoft networking stack can offload one or more of the following tasks to a network adapter that has the support for the offload capabilities.  The following are the supported offload tasks:

Interrupt Moderation

A simple network adapter interrupts the host processor upon receiving a packet or when sending a packet is complete. In many scenarios, where this high processor utilization, it is best to coalesce several packets for each interrupt and reduce the number of times the host processor is interrupted.  Because it is a common mistake to tune interrupt moderation for throughput and hurt response time, most network adapter vendors have implemented dynamic interrupt moderation schemes in their solutions.

Jumbo Frame Support

Supporting larger Maximum Transmission Units (MTUs) and thus larger frame sizes, specifically Jumbo Frames, will reduce the number of trips needed to send the same amount of data.  This results in a significant reduction of CPU utilization.

Receive Side Scaling

For large scale applications, being able to simultaneously process networking requests on multiple processors insures both improved performance through parallelism and CPU load distribution amongst the system processors.  Receive Side Scaling, when supported by the underlying hardware, provides this capability for TCP traffic and the technology is recommended for Web and File Server scenarios where a server is servicing requests from a large number of connections.

Network Adapter Resources

Most network adapters allow administrators to manually configure send and receive resources through the Advanced Networking tab for the adapter.  The most common ones are the receive and send buffers, which most of the time are configured to the low mark resulting in sub-optimal performance.  A small subset of adapters allow for dynamic adjustment for their networking resources so a manual configuration is unnecessary.

Network Adapter Characteristics

When choosing which network adapters to you use, you should always get 64-bit PCI-Express adapters.  Using 32-bit adapters will limit the amount of data the adapter can transfer and will provide sub-optimal performance when copying data into the adapter’s buffers.  Also, an adapter that is not PCI-E will get capped at around 7 Gigabit per seconds pure data transfer excluding headers.  This becomes a bottleneck if you are planning to use 10 Gigabit adapters in your setup and would like to get the full bandwidth out of it.

Network Adapter Tuning

The high performance features discussed earlier about network adapters are necessary for achieving best performance on most scenarios and workloads in single and multiple processor system configurations.  The following are guidelines for where to put the adapter in the system and how to best optimize the distribution of the network adapter interrupts. 

Bus Characteristics

It was mentioned earlier that it is best to place the network adapter in a 64-bit PCI-Express slot.  That guarantees the best performance.  If you will be using PCI or PCI-X slots, then you should try and place your adapter in a slot that does not share the bus with other devices in the system.  Most hardware vendors have diagrams on the inside of the servers or on their websites that describes the layout of the machine.  Sharing a bus with other devices can degrade performance because of latency on the bus and contention.

Interrupt Binding

Interrupts generated by the network adapter can be partitioned to a single or a select group of processors to improve performance.  An example would be partitioning your application threads to run on the same processor where network traffic is processed to preserve cache locality.  In order to bind an adapter’s interrupts to a select group of processors, we recommend using the Interrupt Affinity Policy tool. 

Resources

·         Windows Server 2008 Performance Tuning Guide
http://www.microsoft.com/whdc/system/sysperf/Perf_tun_srv.mspx

·         High Performance Network Adapters and Drivers
http://www.microsoft.com/whdc/device/network/NetAdapters-Drvs.mspx

Ahmed Talat
Performance Manager
Windows Server Performance Team

Thanks for visiting our blog! I’m a development lead in the Windows Server Performance team and I led the performance effort on Hyper-V for Windows Server 2008 over the past three and a half years.

We’ve worked with the product team throughout the Hyper-V development cycle to deliver a competitive product and we’re excited about shipping Hyper-V RTM this year, with the Hyper-V Beta shipping in Windows Server 2008 this week!

Architectural Overview

Hyper-V uses a hypervisor-based architecture and leverages the driver model of Windows for broad hardware support. The hypervisor partitions a server into containers of CPU and memory. As a micro-kernel, it provides mechanisms for inter-partition communication upon which our new high-performance synthetic I/O architecture is built. The root partition owns physical I/O devices and provides services including I/O implemented by the virtualization stack to the child partitions.

The virtualization stack implements emulated I/O devices such as an IDE controller and a DEC 21140A network adapter. However, it is expensive to virtualize such devices. Sending a single I/O might require multiple trips between the virtualization stack and child partition. Instead, Hyper-V exposes synthetic I/O devices that are specially designed for VM environments. These devices are attached to VMBus, which is a plug-and-play capable bus that uses shared memory for efficient inter-partition communication. The Windows guests detect the devices on VMBus and loads the appropriate drivers.

Synthetic I/O in Hyper-V uses a client-server architecture with Virtualization Service Providers (VSPs) in the root and Virtualization Service Clients (VSCs) in the child. This architecture significantly reduces the cost of sending an I/O. Virtual Server customers should observe a major reduction in CPU usage in I/O-intensive loads when they migrate their VMs to Hyper-V.

In addition, we developed operating system enlightenments for Windows Server 2008, which make the NT kernel and memory manager smarter in VM environments, again to reduce the cost of virtualization.

Multi-Processor Guests

For this first blog post, I want to highlight one of the major performance features in Hyper-V: multi-processor virtual machines. Hyper-V supports 4P VMs for Windows Server 2008 guests and 2P VMs for Windows Server 2003 SP2 guests. For more intensive server workloads, you might consider virtualizing them in 2P or 4P VMs on Hyper-V. Of course, you should use multi-processor VMs only if the workload requires it since there is some cost to having additional processors.

However, operating system kernels and drivers use spin locks which do not block and spin until the lock is acquired, with the assumption that the lock is held for a short period. Virtualization breaks this assumption as virtual processors (VPs) are time-sliced. If a VP is preempted while holding a spin lock, other VPs may spin for a long time wasting CPU cycles.

We developed innovations in the hypervisor and Windows Server 2008 kernel to try to prevent long spin wait conditions and also to efficiently detect and handle them when they do occur. We also designed the hypervisor, including the scheduler and memory virtualization logic, to be lock-free on most critical paths to ensure good scalability on multi-processor systems.

As a result, Windows Server 2008 as a 4P guest scales well compared to the physical 4P system. This is one example of Windows Server 2008 as a guest and Hyper-V together providing performance advantages. We plan to continue to improving our scalability on multi-processor systems and multi-processor VMs in subsequent releases.

Closing Thoughts

Thanks for reading this far! I would encourage you to try Hyper-V Beta in Windows Server 2008, which launched this week. And take a look at the Windows Server 2008 and Virtualization web site for more information.

I look forward to writing more on our work on Hyper-V performance. Please add our blog to your RSS feeds!

Regards,

John Sheu

Senior Development Lead

Windows Server Performance Team

Welcome to the Microsoft Windows Server Performance team blog! As the Group Program Manager for the team, I’m delighted to introduce the team and provide the first post to kick off the blog.

The Windows Server Performance team is a part of the Core Operating System Division at Microsoft. Our charter is to understand and improve the performance of Windows Server. As a matter of engineering, the sort of work we do involves:

·         Measurement of performance;

·         Analysis to identify bottlenecks;

·         Identification and implementation of architectural and code changes to improve performance; and

·         To close the loop, verification that the changes we made, did what we expected them to do J

The work gives us an opportunity to see the OS as a whole, and to study the interaction between software components and between hardware and software.

We tend to focus on core scenarios and capabilities in Windows Server (other teams focus on role-specific performance), and look for ways to improve efficiency and scalability. Some of the areas we cover (and will post about) are virtualization, multi-core/multi-proc scalability, file systems (local and remote), network and disk I/O, and server power. We also plan to discuss OS and server application performance in general, and to share some of what we have learnt over the years.

We’ve already published v1.0 of our performance tuning whitepaper for Windows Server 2008 http://www.microsoft.com/whdc/system/sysperf/Perf_tun_srv.mspx - have a look and give us feedback (there’s a feedback link in the document). We will also be doing a Server 2008 performance webcast on Feb 8^th to talk directly about the performance features and improvements in Server 2008.

Additionally, we’ll have some of the team on hand & presenting at the joint launch of Windows Server 2008, SQL Server 2008 and Visual Studio 2008 in Los Angeles on February 27^th. See the launch site for more detail. We hope to see you there!

We are all looking forward to the day when everyone has the opportunity to use the next major release of our OS.

Thanks,
Bill Karagounis
Group Program Manager
Windows Server Performance Team