This application enumerates the properties of the CUDA devices present in the system and displays them in a human readable format.
This application is a very basic demo that implements element by element vector addition. This application is capable of measuring device to device copy bandwidth, host to device copy bandwidth for pageable and page-locked memory, and device to host copy bandwidth for pageable and page-locked memory. Provides detailed statistics about peer-to-peer memory bandwidth amongst GPUs present in the system as well as pinned, unpinned memory bandwidth.
This demo does an efficient all-pairs simulation of a gravitational n-body simulation in CUDA. In this mode, the position and velocity data for all bodies are read from system memory using "zero copy" rather than from device memory.
Apex memmove - the fastest memcpy/memmove on x86/x64 ... EVER, written in C
For a small number of devices 4 or fewer and a large enough number of bodies, bandwidth is not a bottleneck so we can achieve strong scaling across these devices. On creation, randomFog generatesrandom coordinates in spherical coordinate space radius, angle rho, angle theta with curand's XORWOW algorithm. The coordinates are normalized for a uniform distribution through the sphere. The X axis is drawn with blue in the negative direction and yellow positive. The Y axis is drawn with green in the negative direction and magenta positive.
The Z axis is drawn with red in the negative direction and cyan positive. Information furnished is believed to be accurate and reliable. However, NVIDIA Corporation assumes no responsibility for the consequences of use of such information or for any infringement of patents or other rights of third parties that may result from its use.
Specifications mentioned in this publication are subject to change without notice. This publication supersedes and replaces all other information previously supplied. Other company and product names may be trademarks of the respective companies with which they are associated.
All rights reserved. CUDA Toolkit v Demos Below are the demos within the demo suite. Test the bandwidth for device to host, host to device, and device to device transfers Example: measure the bandwidth of device to host pinned memory copies in the range Bytes to Bytes in Byte increments. Arguments: Options Explanation -h print usage -p [0,1] enable or disable pinned memory tests default on -u [0,1] enable or disable unpinned memory tests default off -e [0,1] enable or disable unpinned memory tests default off -u [0,1] enable or disable p2p enabled memory tests default on -d [0,1] enable or disable p2p disabled memory tests default off -a enable all tests -n disable all tests.
The following keys can be used to control the output: Keys Function w Toggle wireframe. Display this help menu. Print results as a CSV. Specify the device device to be used compute cumulative bandwidth on all the devices Specify any particular device to be used. Specify which memory mode to use pageable memory non-pageable system memory.
Specify the mode to use performs a quick measurement measures a user-specified range of values performs an intense shmoo of a large range of values.After watching Day 25 I want to comment on memory bandwidth thing. That number is max CPU supported memory bandwidth. Real bandwidth will be lower.
To test this, I wrote small test application that does memcpy from one memory buffer to other. And I am not seeing max number from spec sheet on my laptop it is Simple memcpy gives me 6. Using SSE2 with following code gives me 3. That means memcpy is better optimized that naive SSE2. Nice and interesting post. Can you test the Linux build against the Win build on the same hardware, please? I am interested, because I am on Linux now, since about the windows 10 preview came along.
I frankly find the new Windows metrostyle obscene, and extremely ugly. IS like:wtf? Every time I see it, I perceive a worse experience then the last time. Its like having a cutting tool to your eyeballs! It has "bad taste" written all over it. Thats what it is.
Why there is a such a strong dependence on the number of loop iterations?
It's ugly as hell, and it's anoying ; But on the other hand I find Linux to feel very slow and unresponsive. Also frequent strange crashes and hangups, not of the os, but the apps I am using. Firefox, System monitor, copying and so on. And I would never consider using this platform for serious coding. Espesially of games or other performant code. And I am frankly in a bit of shock when I hear other people doing it.
What have I missed? Windows is blazing fast if done right, and the MSDN papers have proven me wrong so many times, that I now trust them completely.
Everytime I was suspecting there was something wrong with the API it always turned out that there was not. And that I was just misunderstanding something. I don't know how many times I realized that the real asshole was me.
You can hardly get better information than that.In this and the following post we begin our discussion of code optimization with how to efficiently transfer data between the host and device. This disparity means that your implementation of data transfers between the host and GPU devices can make or break your overall application performance.
We investigate the first three guidelines above in this post, and we dedicate the next post to overlapping data transfers. First I want to talk about how to measure time spent in data transfers without modifying the source code. To measure the time spent in each data transfer, we could record a CUDA event before and after each transfer and use cudaEventElapsedTimeas we described in a previous post.
To profile this code, we just compile it using nvccand then run nvprof with the program filename as an argument. As you can see, nvprof measures the time taken by each of the CUDA memcpy calls.
In the initial stages of porting, data transfers may dominate the overall execution time. Host CPU data allocations are pageable by default. As you can see in the figure, pinned memory is used as a staging area for transfers from the device to the host. We can avoid the cost of the transfer between pageable and pinned host arrays by directly allocating our host arrays in pinned memory.
It is possible for pinned memory allocation to fail, so you should always check for errors. The following code excerpt demonstrates allocation of pinned memory with error checking. Data transfers using host pinned memory use the same cudaMemcpy syntax as transfers with pageable memory.
As you can see, pinned transfers are more than twice as fast as pageable transfers. This is presumably because the faster CPU and chipset reduces the host-side memory copy cost.
You should not over-allocate pinned memory.
Optimizing Memcpy improves speed
How much is too much is difficult to tell in advance, so as with all optimizations, test your applications and the systems they run on for optimal performance parameters. Due to the overhead associated with each transfer, it is preferable to batch many small transfers together into a single transfer.
This is easy to do by using a temporary array, preferably pinned, and packing it with the data to be transferred. For two-dimensional array transfers, you can use cudaMemcpy2D. The arguments here are a pointer to the first destination element and the pitch of the destination array, a pointer to the first source element and pitch of the source array, the width and height of the submatrix to transfer, and the memcpy kind.
There is also a cudaMemcpy3D function for transfers of rank three array sections.
Transfers between the host and device are the slowest link of data movement involved in GPU computing, so you should take care to minimize transfers.However, my title is not without justification! Since optimizing for any one size is a tradeoff.
For less than that I only have a 1 or 2 clock cycle penalty! But for all the other sizes, that title belongs to me! This is a ONCE-off penalty the first time you call them. After that, it's gravy! So there are actually 3 self contained functions here, and they will select; and use the most optimal one automatically!
We detect the presense of SSE4. This is more or less the Core i generation!
How to Optimize Data Transfers in CUDA C/C++
As the function is copying, it constantly issues a prefetching command 4K ahead. This design has never been done like this before! The streaming intrinsics are designed by Intel and AMD for high performance!
They come from a LONG family of functions. Some of the techniques and algorithms I've used have never been published before. Every copy size uses a different technique. So small byte copies have the shortest code path with least jumps! This is NOT normally advisable we don't want the compiler to dictate how we design our functionshowever, when you are designing things for maximum performance, it's important to pay attention to how the code will compile! The strange layout of the functions is a testament to my close observation of the compiler output.
One of the code paths was an AVX bit version. Eventually after about function combinations I was able to consistently beat them. These are only ESTIMATES taken from the original article, which did not include my fastest implementations which were yet to come; so these estimates are from older slower variations. These are very old numbers!Knowing a few details about your system-memory size, cache type, and bus width can pay big dividends in higher performance. The memcpy routine in every C library moves blocks of memory of arbitrary size.
It's used quite a bit in some programs and so is a natural target for optimization. Cross-compiler vendors generally include a precompiled set of standard class libraries, including a basic implementation of memcpy. Unfortunately, since this same code must run on hardware with a variety of processors and memory architectures, it can't be optimized for any specific architecture.
An intimate knowledge of your target hardware and memory-transfer needs can help you write a much more efficient implementation of memcpy. This article will show you how to find the best algorithm for optimizing the memcpy library routine on your hardware. I'll discuss three popular algorithms for moving data within memory and some factors that should help you choose the best algorithm for your needs.
Although I used an Intel XScale processor and evaluation board for this study, the results are general and can be applied to any hardware. A variety of hardware and software factors might affect your decision about a memcpy algorithm. These include the speed of your processor, the width of your memory bus, the availability and features of a data cache, and the size and alignment of the memory transfers your application will make. I'll show you how each of these factors affects the performance of the three algorithms.
But let's first discuss the algorithms themselves. Three basic memcpy algorithms The simplest memory-transfer algorithm just reads one byte at a time and writes that byte before reading the next. We'll call this algorithm byte-by-byte. Listing 1 shows the C code for this algorithm.
As you can see, it has the advantage of implementation simplicity. Byte-by-byte, however, may not offer optimal performance, particularly if your memory bus is wider than 8 bits. An algorithm that offers better performance on wider memory buses, such as the one on the evaluation board I used, can be found in GNU's newlib source code.
I've posted the code here. If the source and destination pointers are both aligned on 4-byte boundaries, my modified-GNU algorithm copies 32 bits at a time rather than 8 bits. Listing 2 shows an implementation of this algorithm. A variation of the modified-GNU algorithm uses computation to adjust for address misalignment.
I'll call this algorithm the optimized algorithm. The optimized algorithm attempts to access memory efficiently, using 4-byte or larger reads-writes.
It operates on the data internally to get the right bytes into the appropriate places. Figure 1 shows a typical step in this algorithm: memory is fetched on naturally aligned boundaries from the source of the block, the appropriate bytes are combined, then written out to the destination's natural alignment.
Figure 1: The optimized algorithm. Note that the optimized algorithm uses some XScale assembly language. You can download this algorithm here. The preload instruction is a hint to the ARM processor that data at a specified address may be needed soon. Processor-specific opcodes like these can help wring every bit of performance out of a critical routine. Knowing your target machine is a virtue when optimizing memcpy.
Having looked at all three of the algorithms in some detail, we can begin to compare their performance under various conditions.Before measuring memory bandwidth with PCM, I think I need to understand the maximum theoretical memory bandwidth.
I thought I had it figured out, but now I have a processor where I don't understand how the maximum numbers make sense. But it also supports up to DDR and has 4 memory channels! One theory is that the E v3 has two memory controllers.
While cpu-world confirms this, it also says that each controller has 2 memory channels, so it still doesn't add up. I'd appreciate any help from the experts over here. Is the number of memory controllers documented by Intel anywhere? I couldn't find it. This buffer chip has two channels on the DIMM side and one interface on the processor side. Under some circumstances, the buffer-to-processor interface can run at 2x the frequency of the buffer-to-DIMM interface.
In this case the bandwidth comes from running the DIMMs at a slightly slower speed, which then allows the buffer-to-processor interface to run at the 2x rate. It looks like the bandwidth comes from:. Thanks, that explains it! Do you know if the existence of this memory buffer documented anywhere? It looks like if you know it exists, you can Google some presentations and articles discussing it, but haven't really seen it mentioned in Intel datasheets or the optimization manuals.
Yes, I agree that the memory buffers are often not discussed as prominently as other features of the platform. Skip to main content. Haswell memory bandwidth. Tim P.
I'm in the process of porting a complex performance oriented application to run on a new dual socket machine. I encountered some performance anomalies while doing so and, after much experimentation, discovered that memory bandwidth on the new machine seems to be substantially slower than I would have expected.
The system is running Ubuntu I wrote a simple memory test utility that repeatedly runs and times a memcpy to determine an average duration and rate:. I then compiled and ran this utility using a 64 MB buffer size significantly larger than the L3 cache size over 10, loops. At the behest of a colleague I tried running the same utility using numactl to localize memory access to only the first numa node. I found this question which is somewhat similar but much more detailed.
From one of the comments: "Populating a 2nd socket forces even local L3 misses to snoop the remote CPU I understand the concept of L3 snooping, but still the overhead compared to the single socket case seems incredibly high to me. Is the behavior that I'm seeing expected? Could someone shed more light on what's happening and what, if anything, I can do about it?
Sign up to join this community.Memory manipulation functions in C
The best answers are voted up and rise to the top. Home Questions Tags Users Unanswered. Ask Question. Asked 1 year, 10 months ago. Active 1 year, 10 months ago. Viewed times. Dave Dave 2 2 bronze badges.