I am using CUDA 6.0 and the OpenCL implementation that comes bundled with the CUDA SDK. I have two identical kernels for each platform (they differ in the platform specific keywords). They only read and write global memory, each thread different location. The launch configuration for CUDA is 200 blocks of 250 threads (1D), which corresponds directly to the configuration for OpenCL - 50,000 global work size and 250 local work size.
The OpenCL code runs faster. Is this possible or am I timing it wrong? My understanding is that the NVIDIA's OpenCL implementation is based on the one for CUDA. I get around 15% better performance with OpenCL.
It would be great if you could suggest why I might be seeing this and perhaps some differences between CUDA and OpenCL as implemented by NVIDIA?
Kernels executing on a modern GPU are almost never compute bound, and are almost always memory bandwidth bound. (Because there are so many compute cores running compared to the available path to memory.)
This means that the performance of a given kernel usually depends largely on the memory access patterns exhibited by the given algorithm.
In practice this makes it very difficult to predict (or even understand) what performance to expect ahead of time.
The differences you observed are likely due to subtle differences in the memory access patterns between the two kernels that result from different optimizations made by the OpenCL vs CUDA toolchain.
To learn how to optimize your GPU kernels it pays to learn the details of the memory caching hardware available to you, and how to use it to best advantage. (e.g., making strategic use of "local" memory caches vs always going directly to "global" memory in OpenCL.)
Related
Can we assign a number of processes (i.e. 100-500 processes) to GPU, each process running on a GPU core?
In my application of video processing, I have to use ffmpeg library to proceed video and audio. If there are like more than 100 or even 500 such independent processes, I guess it's faster to assign each process to a GPU. However, I don't know if we can do it, and to do it, what libraries, tools are necessary? CUDA?
Can we assign a number of processes (i.e. 100-500 processes) to GPU, each process running on a GPU core?
No, you can't. In general it's not possible to schedule anything on a GPU core per se. This level of "scheduling" is handled mainly by the mechanics of the CUDA architecture and runtime system.
The basic idea is to expose parallelism at a fairly low level in your code (e.g. at the loop level) and with proper use of a GPU acceleration syntax (such as CUDA, OpenACC, OpenCL, etc.) the GPU can often make such elements of your program run faster.
But the GPU is not designed to be a drop-in replacement for CPU cores. There is the scheduling factor that I mentioned already, as well as the fact that codes generally need to be compiled for the GPU specifically.
Assume I have Nvidia K40, and for some reason, I want my code only uses portion of the Cuda cores(i.e instead of using all 2880 only use 400 cores for examples), is it possible?is it logical to do this either?
In addition, is there any way to see how many cores are being using by GPU when I run my code? In other words, can we check during execution, how many cores are being used by the code, report likes "task manger" in Windows or top in Linux?
It is possible, but the concept in a way goes against fundamental best practices for cuda. Not to say it couldn't be useful for something. For example if you want to run multiple kernels on the same GPU and for some reason want to allocate some number of Streaming Multiprocessors to each kernel. Maybe this could be beneficial for L1 caching of a kernel that does not have perfect memory access patterns (I still think for 99% of cases manual shared memory methods would be better).
How you could do this, would be to access the ptx identifiers %nsmid and %smid and put a conditional on the original launching of the kernels. You would have to only have 1 block per Streaming Multiprocessor (SM) and then return each kernel based on which kernel you want on which SM's.
I would warn that this method should be reserved for very experienced cuda programmers, and only done as a last resort for performance. Also, as mentioned in my comment, I remember reading that a threadblock could migrate from one SM to another, so behavior would have to be measured before implementation and could be hardware and cuda version dependent. However, since you asked and since I do believe it is possible (though not recommended), here are some resources to accomplish what you mention.
PTS register for SM index and number of SMs...
http://docs.nvidia.com/cuda/parallel-thread-execution/#identifiers
and how to use it in a cuda kernel without writing ptx directly...
https://gist.github.com/allanmac/4751080
Not sure, whether it works with the K40, but for newer Ampere GPUs there is the MIG Multi-Instance-GPU feature to partition GPUs.
https://docs.nvidia.com/datacenter/tesla/mig-user-guide/
I don't know such methods, but would like to get to know.
As to question 2, I suppose sometimes this can be useful. When you have complicated execution graphs, many kernels, some of which can be executed in parallel, you want to load GPU fully, most effectively. But it seems on its own GPU can occupy all SMs with single blocks of one kernel. I.e. if you have a kernel with 30-blocks grid and 30 SMs, this kernel can occupy entire GPU. I believe I saw such effect. Really this kernel will be faster (maybe 1.5x against 4 256-threads blocks per SM), but this will not be effective when you have another work.
GPU can't know whether we are going to run another kernel after this one with 30 blocks or not - whether it will be more effective to spread it onto all SMs or not. So some manual way to say this should exist
As to question 3, I suppose GPU profiling tools should show this, Visual Profiler and newer Parallel Nsight and Nsight Compute. But I didn't try. This will not be Task manager, but a statistics for kernels that were executed by your program instead.
As to possibility to move thread blocks between SMs when necessary,
#ChristianSarofeen, I can't find mentions that this is possible. Quite the countrary,
Each CUDA block is executed by one streaming multiprocessor (SM) and
cannot be migrated to other SMs in GPU (except during preemption,
debugging, or CUDA dynamic parallelism).
https://developer.nvidia.com/blog/cuda-refresher-cuda-programming-model/
Although starting from some architecture there is such thing as preemption. As I remember NVidia advertised it in the following way. Let's say you made a game that run some heavy kernels (say for graphics rendering). And then something unusual happened. You need to execute some not so heavy kernel as fast as possible. With preemption you can unload somehow running kernels and execute this high priority one. This increases execution time (of this high pr. kernel) a lot.
I also found such thing:
CUDA Graphs present a new model for work submission in CUDA. A graph
is a series of operations, such as kernel launches, connected by
dependencies, which is defined separately from its execution. This
allows a graph to be defined once and then launched repeatedly.
Separating out the definition of a graph from its execution enables a
number of optimizations: first, CPU launch costs are reduced compared
to streams, because much of the setup is done in advance; second,
presenting the whole workflow to CUDA enables optimizations which
might not be possible with the piecewise work submission mechanism of
streams.
https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#cuda-graphs
I do not believe kernels invocation take a lot of time (of course in case of a stream of kernels and if you don't await for results in between). If you call several kernels, it seems possible to send all necessary data for all kernels while the first kernel is executing on GPU. So I believe NVidia means that it runs several kernels in parallel and perform some smart load-balancing between SMs.
I downloaded CUDA 6.0 RC and tested the new unified memory by using "cudaMallocManaged" in my application.However, I found this kernel is slowed down.
Using cudaMalloc followed by cudaMemcpy is faster (~0.56), compared to cudaMallocManaged (~0.63).Is this expected?
One of the website claims that cudaMallocManged is for "faster prototyping of cuda kernel", so I was wondering which is a better option for application in terms of performance?
Thanks.
cudaMallocManaged() is not about speeding up your application (with a few exceptions or corner cases, some are suggested below).
Today's implementation of Unified Memory and cudaMallocManaged will not be faster than intelligently written code written by a proficient CUDA programmer, to do the same thing. The machine (cuda runtime) is not smarter than you are as a programmer. cudaMallocManaged does not magically make the PCIE bus or general machine architectural limitations disappear.
Fast prototyping refers to the time it takes you to write the code, not the speed of the code.
cudaMallocManaged may be of interest to a proficient cuda programmer in the following situations:
You're interested in quickly getting a prototype together -i.e. you don't care about the last ounce of performance.
You are dealing with a complicated data structure which you use infrequently (e.g. a doubly linked list) which would otherwise be a chore to port to CUDA (since deep copies using ordinary CUDA code tend to be a chore). It's necessary for your application to work, but not part of the performance path.
You would ordinarily use zero-copy. There may be situations where using cudaMallocManaged could be faster than a naive or inefficient zero-copy approach.
You are working on a Jetson device.
cudaMallocManaged may be of interest to a non-proficient CUDA programmer in that it allows you to get your feet wet with CUDA along a possibly simpler learning curve. (However, note that naive usage of cudaMallocManaged may result in a CUDA kernels running slower than expected, see here and here.)
Although Maxwell is mentioned in the comments, CUDA UM will offer major new features with the Pascal generation of GPUs, in some settings, for some GPUs. In particular, Unified Memory in these settings will no longer be limited to the available GPU device memory, and the memory handling granularity will drop to the page level even when the kernel is running. You can read more about it here.
I have some experience with nVIDIA CUDA and am now thinking about learning openCL too. I would like to be able to run my programs on any GPU. My question is: does every GPU use the same architecture as nVIDIA (multi-processors, SIMT stracture, global memory, local memory, registers, cashes, ...)?
Thank you very much!
Starting with your stated goal:
"I would like to be able to run my programs on any GPU."
Then yes, you should learn OpenCL.
In answer to your overall question, other GPU vendors do use different architectures than Nvidia GPUs. In fact, GPU designs from a single vendor can vary by quite a bit, depending on the model.
This is one reason that a given OpenCL code may perform quite differently (depending on your performance metric) from one GPU to the next. In fact, to achieve optimized performance on any GPU, an algorithm should be "profiled" by varying, for example, local memory size, to find the best algorithm settings for a given hardware design.
But even with these hardware differences, the goal of OpenCL is to provide a level of core functionality that is supported by all devices (CPUs, GPUs, FPGAs, etc) and include "extensions" which allow vendors to expose unique hardware features. Although OpenCL cannot hide significant differences in hardware, it does guarantee portability. This makes it much easier for a developer to start with an OpenCL program tuned for one device and then develop a program optimized for another architecture.
To complicate matters with identifying hardware differences, the terminology used by CUDA is different than that used by OpenCL, for example, the following are roughly equivalent in meaning:
CUDA: OpenCL:
Thread Work-item
Thread block Work-group
Global memory Global memory
Constant memory Constant memory
Shared memory Local memory
Local memory Private memory
More comparisons and discussion can be found here.
You will find that the kinds of abstraction provided by OpenCL and CUDA are very similar. You can also usually count on your hardware having similar features: global mem, local mem, streaming multiprocessors, etc...
Switching from CUDA to OpenCL, you may be confused by the fact that many of the same concepts have different names (for example: CUDA "warp" == OpenCL "wavefront").
I coded a program to create a color lookup table. I did it in CUDA and OpenCL, from my point of view both programs are pretty much the same, i.e. use the same amount of constant memory, global memory, same loops and branching code, etc.
I measure the running time and CUDA performed slightly better than OpenCL. My question is if using CUDA+NvidiaGPU is faster than OpenCL+NvidiaGPU because CUDA is the native way of programming such GPU?
Could you share some links to info related on this topic?
OpenCL and CUDA are equally fast if they are tweaked correctly for the target architecture. However, tweaking may negatively impact portability.
Links:
http://arxiv.org/ftp/arxiv/papers/1005/1005.2581.pdf
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=6047190&tag=1