Performance profiling in Visual Studio under Virtual Machine (Sampling VS Instrumentation)


The bad news is that Visual Studio’s own, excellent Performance Profiler does not support Sampling under any virtual machine environment – only Instrumentation.

It will not fail, but will not be able to collect any performance data if you try to do sampling while running in a virtual machine (like Parallels Desktop or VMWare Player / Workstation).

Visual Studio - Branch Mispredition with Sampling
Sampling accesses the hardware directly, so it is very fast and detailed: it gives you line level performance audit and even can give branch misprediction results if you are interested (to see the latter one, go to Properties of the Performance Explorer, go to Sampling section, switch to Performance Counter them open the Branch Events tree node).



On the other hand, instrumentations needs a special build, it basically injects data collection functions into your compiled code. Hence the runtime performance of the instrumented code is generally a magnitude (10x) slower than with Sampling and not detailed at all: only function level, and it only measures the time spent in each function. For instance, if you are writing a multi-threaded app, it is likely that you will spend some time in Thread.Sleep to synchronize your threads. This is idle time for the CPU so sampling does not really care, but it will be the biggest “problem” according to the Instrumentation - which can be really misleading.

So, if you want to measure performance, boot directly into Windows rather than any virtual machine.

Comments

  1. SevZJanuary 30, 2015 at 9:46 AM

    I've tried both sampling and instrumented profiling and besides speed of execution, the major difference that I see is that instrumented code provides absolute timings granular to .01 msec, whereas sampling will only tell you the percentage of time is spent in each function. So for a quick glance at the most time-consuming functions, sampling is ok but when absolute length of time within a function is needed, you need to use instrumentation. As far as the performance impact of instrumentation, in my CPU-bound app I see that simply adding instrumentation to your code reduces performance by a factor of 2 and turning on profiling further reduces it by a factor of 3.5, therefore the total impact to the code is a factor of 7 which is close to this blog post's estimate of 10x.

    ReplyDelete

Post a Comment

Popular posts from this blog

MurMurHash3, an ultra fast hash algorithm for C# / .NET

Image
Finding good hash functions for larger data sets is always challenging. The well know hashes, such as MD5, SHA1, SHA256 are fairly slow with large data processing and their added extra functions (such as being cryptographic hashes) isn’t always required either. Performance and low collision rate on the other hand is very important, so many new hash functions were inverted in the past few years. One of the most notable ones is MurMurHash3, which is an improved version of its predecessor (v2). The new version can create both 32 bit and 128 bit hash values, making it suitable for a wide range of applications. 64 bit architecture The 32 bit hash isn’t too important for large amount of data matching, so I only worked with and tested the 128 bit version. The latter one is optimized for x86-64 architecture, giving amazing speeds for data processing. As a 128 bit value consists of two 64 integers, the processor is fairly good with working with such large hash values – they are not byte...
. . .

Read more »

Quick select algorithm - find the Kth element in a list in linear time

Image
Quick select algorithm (Hoare's selection algorithm) – select the Kth element or  the first K element  from a list in linear time Working with large datasets is always painful, especially when it needs to be displayed in a ‘human readable’ format. It is a very frequent task to display only the largest, newest, most expensive etc. items. While sorting the whole dataset definitely gives a correct result, it is much slower than it needs to be – it needs at least O(n*log(n)) time and an it often uses recursion for the sorting, so in practice it can be quite slow. The quick select algorithm can get the top K element from a list of N items in linear time, O(n), with a very reasonable multiplication factor. The quick select does not use recursion so the performance is great for even large datasets. Algorithm The idea of the quick select is quite simple: just like with quicksort, select a random element from the list, and place every item that is smaller to the first half o...
. . .

Read more »

ESP32 - send a push notification from the Arduino ESP32 device to your phone

Image
Sending a push notification to your phone from an event from an ESP32 is simple. What you'll need (if you are using a Mac for development): Arduino IDE Arduino for ESP32 Serial port simulator for ESP32 to upload your code to the board Wifi network IFTTT account IFTTT app on your phone Wifi network that your ESP32 board can access Architecture Create an IFTTT applet This should read something like this: If Maker Event "notification", then Send a notification from the IFTTT app Change the notification template Go to the settings of your new IFTTT applet and change the template to include "Value1", like this: Get your Webhook endpoint in IFTTT On your applet, click the triangle looking thing (webhook), then Settings, or simply go to this URL  https://ifttt.com/maker_webhooks/settings   Visit the private URL in your browser, replace the trigger name with "notification", so it reads like ...trigger/notification/ with/... and in the Value1 field type in a ...
. . .

Read more »