Closed. This question needs to be more focused. It is not currently accepting answers.
Want to improve this question? Update the question so it focuses on one problem only by editing this post.
Closed 8 years ago.
Improve this question
I want to measure scalability and performances of one mpi program I wrote. Till now I used the MPI_Barrier function and the stopwatch library in order to count the time. The thing is that the computation time depends a lot on the current use of my cpu and ram so all the time I get different results. Moreover my program runs on a virtual machine vmware which I need in order to use Unix.
I wanted to ask...how can I have an objective measure of the times? I want to see if my program has a good scalability or not.
In general, the way most people measure time in their MPI programs is to use MPI_WTIME since it's supposed to be a portable way to get the system time. That will give you a decent realtime result.
If you're looking to measure CPU time instead of real time, that's a very different and much more difficult problem. Usually the way most people handle that is to run their benchmarks on an otherwise quiet system.
Related
Closed. This question needs to be more focused. It is not currently accepting answers.
Want to improve this question? Update the question so it focuses on one problem only by editing this post.
Closed 2 years ago.
Improve this question
Recent developments in gpus (the past few generations) allow them to be programmed. Languages like Cuda, openCL, openACC are specific to this hardware. In addition, certain games allow programming shaders which function in the rendering of images in the graphics pipeline. Just as code intended for a cpu can cause unintended execution resulting a vulnerability, I wonder if a game or other code intended for a gpu can result in a vulnerability.
The benefit a hacker would get from targeting the GPU is "free" computing power without having to deal with the energy cost. The only practical scenario here is crypto-miner viruses, see this article for example. I don't know details on how they operate, but the idea is to use the GPU to mine crypto-currencies in the background, since GPUs are much more efficient than CPUs at this. These viruses will cause substential energy consumption if unnoticed.
Regarding an application running on the GPU causing/using a vulnerability, the use-cases here are rather limited since security-relevant data usually is not processed on GPUs.
At most you could deliberately make the graphics driver crash and this way sabotage other programs from being properly executed.
There already are plenty security mechanisms prohibiting reading other processes' VRAM etc., but there always is some way around.
Closed. This question needs to be more focused. It is not currently accepting answers.
Want to improve this question? Update the question so it focuses on one problem only by editing this post.
Closed 2 years ago.
Improve this question
I need to get the Processes consuming CPU the most and over what time. Is this possible using any counter or script?
This at least gets you the info on who's using up the CPU. As to when, well that's another question entiresly.
I think you should configure a data collector set in Performance Monitor (PerfMon). You can collect the counter "\Process(*)% Processor Time". You can roll over the collector files for analysis later and hence see process performance over time.
When you look at the files later the graphs should make it easier to find the process that's consuming more CPU. I can't bang out a full tutorial at the moment, but a simple google search should turn up plenty of instructional info.
I will say the biggest challenge is configuring the schedule just right to make sure you capturing all the data you need. If that starts getting confusing there's a folder buried in Task Manager called PLA. That's for Performance Logs & Alerts. You should find a job there that correlates to your collector. It may be easier to work on the schedule there...
Thanks.
Closed. This question needs details or clarity. It is not currently accepting answers.
Want to improve this question? Add details and clarify the problem by editing this post.
Closed 5 years ago.
Improve this question
I studied how to optimize algorithms for multiprocessor systems. Now I would understand in main lines how these algorithms can be transformed into code.
I know that exist some libraries MPI based that helps the developement of software portable to different type of systems, but is right the word "portable" that makes me confused: how the program can be authomatically adapted to an arbitrary number of processors at runtime, since this is an option of mpirun? How the software can decide the proper topology (mesh, hypercube, tree, ring, etc)? The programmer can specify the preferred topology through MPI?
you start the application with a fixed number of cores. Thus, you cannot automatically adapted to an arbitrary number of processors at runtime.
You can tune your software to the topology of your cluster. This is really advanced and for sure not portable. It only makes sense if you have a fixed cluster and are striving for the last bit of performance.
Best regards, Georg
Closed. This question needs to be more focused. It is not currently accepting answers.
Want to improve this question? Update the question so it focuses on one problem only by editing this post.
Closed 7 years ago.
Improve this question
It seems like for special tasks GPU can be 10x or more powerful than the CPU.
Can we make this power more accessible and utilise it for common programming?
Like having cheap server easily handling millions of connections? Or on-the-fly database analytics? Map/reduce/Hadoop/Storm - like stuff with 10x throughput? Etc?
Is there any movement in such direction? Any new programming languages or programming paradigms that will utilise it?
CUDA or OpenCL are good implementations of GPU programming.
GPU programming uses Shaders to process input buffers and almost instantly generate result buffers. Shaders are small algorithms units, mostly working with float values, which contains their own data context (input buffers and constants) to produce results. Each Shader is isolated from the other Shaders during a task, but you can chain them if required.
GPU programming won't be good at handling HTTP requests since this is mostly a complex sequential process, but it will be amazing to process, for example, a photo or a neural network.
As soon as you can chunk your data into tiny parallel units, then yes it can help. The CPU will remain better for complex sequential tasks.
Colonel Thirty Two links to a long and interesting answer about this if you want more informations : https://superuser.com/questions/308771/why-are-we-still-using-cpus-instead-of-gpus
Closed. This question needs to be more focused. It is not currently accepting answers.
Want to improve this question? Update the question so it focuses on one problem only by editing this post.
Closed 10 months ago.
Improve this question
If a website is experiencing performance issues all of a sudden, what can be the reasons behind it?
According to me database can one reason or space on server can be one of few reasons, I would like to know more about it.
There can be n number of reasons and n depends on your specification
According to what you have specified you can have a look at,
System counters of webserver/appserver like cpu, memory, paging, io, disk
What changes you did to application if any, were those changes performance costly i.e. have a round of analysis on those changes to check whether any improvement is required.
If system counters are choking then check which one is bottleneck and try to resolve it.
Check all layers/tiers of application i.e. app server, database, directory etc.
if database is bottleneck then identify costly queries and apply indexes & other DB tuning
If app server is choking then, you need to identify & improve the method which is resource heavy.
Performance tuning is not a fast track process, it takes time, identify bottlenecks and try to solve it and repeat the process until you get desired performance.