Please see the MSDN documentation for these routines for more information. If B has not finished writing its element before A tries to read it, we have a race condition, which can lead to undefined behavior and incorrect results. The results of these calculations can frequently differ from pure 64-bit operations performed on the CUDA device. CUDA Binary (cubin) Compatibility, 15.4. Package managers facilitate this process but unexpected issues can still arise and if a bug is found, it necessitates a repeat of the above upgrade process. This is important for a number of reasons; for example, it allows the user to profit from their investment as early as possible (the speedup may be partial but is still valuable), and it minimizes risk for the developer and the user by providing an evolutionary rather than revolutionary set of changes to the application. For devices of compute capability 2.x, there are two settings, 48KBshared memory / 16KB L1 cache, and 16KB shared memory / 48KB L1 cache. NVIDIA accepts no liability for inclusion and/or use of NVIDIA products in such equipment or applications and therefore such inclusion and/or use is at customers own risk. For devices of compute capability 2.0, the warp size is 32 threads and the number of banks is also 32. After the application is dynamically linked against the CUDA Runtime, this version of the runtime library should be bundled with the application. Texture references that are bound to CUDA arrays can be written to via surface-write operations by binding a surface to the same underlying CUDA array storage). When an application is built for multiple compute capabilities simultaneously (using several instances of the -gencode flag to nvcc), the binaries for the specified compute capabilities are combined into the executable, and the CUDA Driver selects the most appropriate binary at runtime according to the compute capability of the present device. (See also the__launch_bounds__ qualifier discussed in Execution Configuration of the CUDA C++ Programming Guide to control the number of registers used on a per-kernel basis.). This code reverses the data in a 64-element array using shared memory. A place where magic is studied and practiced? This Link TLB has a reach of 64 GB to the remote GPUs memory. For more details refer to the memcpy_async section in the CUDA C++ Programming Guide. The combined L1 cache capacity for GPUs with compute capability 8.6 is 128 KB. Resources stay allocated to each thread until it completes its execution. It is however usually more effective to use a high-level programming language such as C++. cudaDeviceSynchronize()blocks the calling CPU thread until all CUDA calls previously issued by the thread are completed. See https://developer.nvidia.com/nvidia-management-library-nvml for additional information. Declare shared memory in CUDA C/C++ device code using the__shared__variable declaration specifier. Strong Scaling and Amdahls Law describes strong scaling, which allows us to set an upper bound for the speedup with a fixed problem size. My code is GPL licensed, can I issue a license to have my code be distributed in a specific MIT licensed project? An upgraded driver matching the CUDA runtime version is currently required for those APIs. CUDA Compatibility Across Minor Releases, 15.4.1. Strong scaling is usually equated with Amdahls Law, which specifies the maximum speedup that can be expected by parallelizing portions of a serial program. For example, in the standard CUDA Toolkit installation, the files libcublas.so and libcublas.so.5.5 are both symlinks pointing to a specific build of cuBLAS, which is named like libcublas.so.5.5.x, where x is the build number (e.g., libcublas.so.5.5.17). nvidia-smi is targeted at Tesla and certain Quadro GPUs, though limited support is also available on other NVIDIA GPUs. A Sequential but Misaligned Access Pattern, 9.2.2.2. Shared memory accesses, in counterpoint, are usually worth optimizing only when there exists a high degree of bank conflicts. These are the same contexts used implicitly by the CUDA Runtime when there is not already a current context for a thread. When attempting to optimize CUDA code, it pays to know how to measure performance accurately and to understand the role that bandwidth plays in performance measurement. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. This approach is most straightforward when the majority of the total running time of our application is spent in a few relatively isolated portions of the code. To minimize bank conflicts, it is important to understand how memory addresses map to memory banks. All CUDA Runtime API calls return an error code of type cudaError_t; the return value will be equal to cudaSuccess if no errors have occurred. The C++ host code generated by nvcc utilizes the CUDA Runtime, so applications that link to this code will depend on the CUDA Runtime; similarly, any code that uses the cuBLAS, cuFFT, and other CUDA Toolkit libraries will also depend on the CUDA Runtime, which is used internally by these libraries. Because the driver may interleave execution of CUDA calls from other non-default streams, calls in other streams may be included in the timing. If you want to communicate (i.e. An optimized handling of strided accesses using coalesced reads from global memory. Handling New CUDA Features and Driver APIs, 15.4.1.4. (See Data Transfer Between Host and Device.) The CUDA Toolkit includes a number of such libraries that have been fine-tuned for NVIDIA CUDA GPUs, such as cuBLAS, cuFFT, and so on. Such a pattern is shown in Figure 3. Using asynchronous copies does not use any intermediate register. A kernel to illustrate non-unit stride data copy. For optimal performance, users should manually tune the NUMA characteristics of their application. Application binaries rely on CUDA Driver API interface and even though the CUDA Driver API itself may also have changed across toolkit versions, CUDA guarantees Binary Compatibility of the CUDA Driver API interface. If cudaGetDeviceCount() reports an error, the application should fall back to an alternative code path. Timeline comparison for copy and kernel execution. First introduced in CUDA 11.1, CUDA Enhanced Compatibility provides two benefits: By leveraging semantic versioning across components in the CUDA Toolkit, an application can be built for one CUDA minor release (for example 11.1) and work across all future minor releases within the major family (i.e. Devices of compute capability 8.6 have 2x more FP32 operations per cycle per SM than devices of compute capability 8.0. For more information please refer to the section on Async Copy in the CUDA C++ Programming Guide. Access to shared memory is much faster than global memory access because it is located on a chip. The repeated reading of the B tile can be eliminated by reading it into shared memory once (Improvement by reading additional data into shared memory). Max and current clock rates are reported for several important clock domains, as well as the current GPU performance state (pstate). Before implementing lower priority recommendations, it is good practice to make sure all higher priority recommendations that are relevant have already been applied. Increased Memory Capacity and High Bandwidth Memory, 1.4.2.2. The NVIDIA Nsight Visual Studio Edition for Microsoft Windows 7, Windows HPC Server 2008, Windows 8.1, and Windows 10 is available as a free plugin for Microsoft Visual Studio; see: https://developer.nvidia.com/nsight-visual-studio-edition. Support for TF32 Tensor Core, through HMMA instructions. Then with a tile size of 32, the shared memory buffer will be of shape [32, 32]. The peak theoretical bandwidth between the device memory and the GPU is much higher (898 GB/s on the NVIDIA Tesla V100, for example) than the peak theoretical bandwidth between host memory and device memory (16 GB/s on the PCIe x16 Gen3). exchange data) between threadblocks, the only method is to use global memory. As a result, all modern processors require parallel code in order to achieve good utilization of their computational power. Conditionally use features to remain compatible against older drivers. Code that cannot be sufficiently parallelized should run on the host, unless doing so would result in excessive transfers between the host and the device. The library should follow semantic rules and increment the version number when a change is made that affects this ABI contract. Note that cudaSetDeviceFlags() must be called prior to setting a device or making a CUDA call that requires state (that is, essentially, before a context is created). Generally, accessing a register consumes zero extra clock cycles per instruction, but delays may occur due to register read-after-write dependencies and register memory bank conflicts. The size is implicitly determined from the third execution configuration parameter when the kernel is launched. Bandwidth - the rate at which data can be transferred - is one of the most important gating factors for performance. The number of copy engines on a GPU is given by the asyncEngineCount field of the cudaDeviceProp structure, which is also listed in the output of the deviceQuery CUDA Sample. On devices that have this capability, the overlap once again requires pinned host memory, and, in addition, the data transfer and kernel must use different, non-default streams (streams with non-zero stream IDs). As seen above, in the case of misaligned sequential accesses, caches help to alleviate the performance impact. \times (4096/8) \times 2 \right) \div 10^{9} = 898\text{GB/s}\). All rights reserved. The --ptxas options=v option of nvcc details the number of registers used per thread for each kernel. These transfers are costly in terms of performance and should be minimized. The cubins are architecture-specific. To verify the exact DLL filename that the application expects to find at runtime, use the dumpbin tool from the Visual Studio command prompt: Once the correct library files are identified for redistribution, they must be configured for installation into a location where the application will be able to find them. The warp size is 32 threads and the number of banks is also 32, so bank conflicts can occur between any threads in the warp. Computing a row of a tile. The CUDA event API provides calls that create and destroy events, record events (including a timestamp), and convert timestamp differences into a floating-point value in milliseconds. This variant simply uses the transpose of A in place of B, so C = AAT. Compiler JIT Cache Management Tools, 18.1. These bindings expose the same features as the C-based interface and also provide backwards compatibility. Like the other calls in this listing, their specific operation, parameters, and return values are described in the CUDA Toolkit Reference Manual. The use of shared memory is illustrated via the simple example of a matrix multiplication C = AB for the case with A of dimension Mxw, B of dimension wxN, and C of dimension MxN. If this set-aside portion is not used by persistent accesses, then streaming or normal data accesses can use it. Applications compiled with CUDA toolkit versions as old as 3.2 will run on newer drivers. They can be distinguished by their names: some have names with prepended underscores, whereas others do not (e.g., __functionName() versus functionName()). PTX defines a virtual machine and ISA for general purpose parallel thread execution. For example, servers that have two 32 core processors can run only 64 threads concurrently (or small multiple of that if the CPUs support simultaneous multithreading). A useful counterpart to the reference comparisons described above is to structure the code itself in such a way that is readily verifiable at the unit level. Missing dependencies is also a binary compatibility break, hence you should provide fallbacks or guards for functionality that depends on those interfaces. Various dynamic and static information is reported, including board serial numbers, PCI device IDs, VBIOS/Inforom version numbers and product names. Randomly accessing. Low Medium Priority: Use signed integers rather than unsigned integers as loop counters. As mentioned in the PTX section, the compilation of PTX to device code lives along with the CUDA driver, hence the generated PTX might be newer than what is supported by the driver on the deployment system. Overall, best performance is achieved when using asynchronous copies with an element of size 8 or 16 bytes. In these cases, no warp can ever diverge. Asynchronous Copy from Global Memory to Shared Memory CUDA 11.0 introduces an async-copy feature that can be used within device code . Each floating-point arithmetic operation involves a certain amount of rounding. For Windows, the /DELAY option is used; this requires that the application call SetDllDirectory() before the first call to any CUDA API function in order to specify the directory containing the CUDA DLLs. Furthermore, the pinning of system memory is a heavyweight operation compared to most normal system memory allocations, so as with all optimizations, test the application and the systems it runs on for optimal performance parameters. This difference is illustrated in Figure 13. Optimizations can be applied at various levels, from overlapping data transfers with computation all the way down to fine-tuning floating-point operation sequences. The amount of performance benefit an application will realize by running on CUDA depends entirely on the extent to which it can be parallelized. Staged concurrent copy and execute shows how the transfer and kernel execution can be broken up into nStreams stages. A device in which work is poorly balanced across the multiprocessors will deliver suboptimal performance. Data that cannot be laid out so as to enable coalescing, or that doesnt have enough locality to use the L1 or texture caches effectively, will tend to see lesser speedups when used in computations on GPUs. How to manage this resource utilization is discussed in the final sections of this chapter. However, if multiple threads requested addresses map to the same memory bank, the accesses are serialized. (BFloat16 only supports FP32 as accumulator), unsigned char/signed char (8-bit precision). A stream is simply a sequence of operations that are performed in order on the device. In fact, local memory is off-chip. Best performance with synchronous copy is achieved when the copy_count parameter is a multiple of 4 for all three element sizes. To understand the performance difference between synchronous copy and asynchronous copy of data from global memory to shared memory, consider the following micro benchmark CUDA kernels for demonstrating the synchronous and asynchronous approaches. A key aspect of correctness verification for modifications to any existing program is to establish some mechanism whereby previous known-good reference outputs from representative inputs can be compared to new results. Multiple kernels executing at the same time is known as concurrent kernel execution. When using a shared or static library, follow the release notes of said library to determine if the library supports minor version compatibility. The right value for minBlocksPerMultiprocessor should be determined using a detailed per kernel analysis. Developers are notified through deprecation and documentation mechanisms of any current or upcoming changes. The bandwidthTest CUDA Sample shows how to use these functions as well as how to measure memory transfer performance. Furthermore, if accesses by the threads of the warp had been permuted within or accross the four segments, still only four 32-byte transactions would have been performed by a device with compute capability 6.0 or higher. Page-locked or pinned memory transfers attain the highest bandwidth between the host and the device. Figure 6 illustrates how threads in the CUDA device can access the different memory components. Applying Strong and Weak Scaling, 6.3.2. When working with a feature exposed in a minor version of the toolkit, the feature might not be available at runtime if the application is running against an older CUDA driver. TO THE EXTENT NOT PROHIBITED BY LAW, IN NO EVENT WILL NVIDIA BE LIABLE FOR ANY DAMAGES, INCLUDING WITHOUT LIMITATION ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, PUNITIVE, OR CONSEQUENTIAL DAMAGES, HOWEVER CAUSED AND REGARDLESS OF THE THEORY OF LIABILITY, ARISING OUT OF ANY USE OF THIS DOCUMENT, EVEN IF NVIDIA HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. Here cudaEventRecord() is used to place the start and stop events into the default stream, stream 0. For an existing project, the first step is to assess the application to locate the parts of the code that are responsible for the bulk of the execution time. This capability (combined with thread synchronization) has a number of uses, such as user-managed data caches, high-performance cooperative parallel algorithms (parallel reductions, for example), and tofacilitate global memory coalescing in cases where it would otherwise not be possible. This should be our first candidate function for parallelization. In this code, the canMapHostMemory field of the structure returned by cudaGetDeviceProperties() is used to check that the device supports mapping host memory to the devices address space. The multidimensional aspect of these parameters allows easier mapping of multidimensional problems to CUDA and does not play a role in performance. On the other hand, if the data is only accessed once, such data accesses can be considered to be streaming. These many-way bank conflicts are very expensive. Obtaining the right answer is clearly the principal goal of all computation. (Factorization). Other company and product names may be trademarks of the respective companies with which they are associated. Sample CUDA configuration data reported by deviceQuery. NVIDIA products are not designed, authorized, or warranted to be suitable for use in medical, military, aircraft, space, or life support equipment, nor in applications where failure or malfunction of the NVIDIA product can reasonably be expected to result in personal injury, death, or property or environmental damage. Understanding the Programming Environment, 15. By comparison, the smallest executable unit of parallelism on a CUDA device comprises 32 threads (termed a warp of threads). sorting the queues) and then a single threadblock would perform the clean-up tasks such as collecting the queues and processing in a single threadblock. Increment major versions when there are ABI breaking changes such as API deprecation and modifications. After each round of application parallelization is complete, the developer can move to optimizing the implementation to improve performance. The current board power draw and power limits are reported for products that report these measurements. //Set the attributes to a CUDA stream of type cudaStream_t, Mapping Persistent data accesses to set-aside L2 in sliding window experiment, /*Each CUDA thread accesses one element in the persistent data section. Floating Point Math Is not Associative, 8.2.3. For example, it may be desirable to use a 64x64 element shared memory array in a kernel, but because the maximum number of threads per block is 1024, it is not possible to launch a kernel with 64x64 threads per block. This microbenchmark uses a 1024 MB region in GPU global memory. This ensures your code is compatible. It is important to use the same divisor when calculating theoretical and effective bandwidth so that the comparison is valid. The CUDA Driver API has a versioned C-style ABI, which guarantees that applications that were running against an older driver (for example CUDA 3.2) will still run and function correctly against a modern driver (for example one shipped with CUDA 11.0). As can be seen from these tables, judicious use of shared memory can dramatically improve performance. .Y stands for the minor version - Introduction of new APIs, deprecation of old APIs, and source compatibility might be broken but binary compatibility is maintained. Notwithstanding any damages that customer might incur for any reason whatsoever, NVIDIAs aggregate and cumulative liability towards customer for the products described herein shall be limited in accordance with the Terms of Sale for the product. The __pipeline_wait_prior(0) will wait until all the instructions in the pipe object have been executed. Prior to UVA, an application had to keep track of which pointers referred to device memory (and for which device) and which referred to host memory as a separate bit of metadata (or as hard-coded information in the program) for each pointer. One or more compute capability versions can be specified to the nvcc compiler while building a file; compiling for the native compute capability for the target GPU(s) of the application is important to ensure that application kernels achieve the best possible performance and are able to use the features that are available on a given generation of GPU. For example, on devices of compute capability 7.0 each multiprocessor has 65,536 32-bit registers and can have a maximum of 2048 simultaneous threads resident (64 warps x 32 threads per warp). NVIDIA makes no representation or warranty that products based on this document will be suitable for any specified use. Local memory is used only to hold automatic variables. The performance on a device of any compute capability can be improved by reading a tile of A into shared memory as shown in Using shared memory to improve the global memory load efficiency in matrix multiplication. When accessing uncached local or global memory, there are hundreds of clock cycles of memory latency. For example, the asyncEngineCount field of the device property structure indicates whether overlapping kernel execution and data transfers is possible (and, if so, how many concurrent transfers are possible); likewise, the canMapHostMemory field indicates whether zero-copy data transfers can be performed. (The performance advantage sinpi() has over sin() is due to simplified argument reduction; the accuracy advantage is because sinpi() multiplies by only implicitly, effectively using an infinitely precise mathematical rather than a single- or double-precision approximation thereof.). Here, the effective bandwidth is in units of GB/s, Br is the number of bytes read per kernel, Bw is the number of bytes written per kernel, and time is given in seconds. The various principal traits of the memory types are shown in Table 1. Cached in L1 and L2 by default on devices of compute capability 6.0 and 7.x; cached only in L2 by default on devices of lower compute capabilities, though some allow opt-in to caching in L1 as well via compilation flags. Non-default streams are required for this overlap because memory copy, memory set functions, and kernel calls that use the default stream begin only after all preceding calls on the device (in any stream) have completed, and no operation on the device (in any stream) commences until they are finished. Optimizing memory usage starts with minimizing data transfers between the host and the device because those transfers have much lower bandwidth than internal device data transfers. Single-precision floats provide the best performance, and their use is highly encouraged. It would have been more so if adjacent warps had not exhibited such a high degree of reuse of the over-fetched cache lines. Recommendations for taking advantage of minor version compatibility in your application, 16.4. The L2 cache set-aside size for persisting accesses may be adjusted, within limits: Mapping of user data to L2 set-aside portion can be controlled using an access policy window on a CUDA stream or CUDA graph kernel node. One method for doing so utilizes shared memory, which is discussed in the next section. All kernel launches are asynchronous, as are memory-copy functions with the Async suffix on their names. The details of various CPU timing approaches are outside the scope of this document, but developers should always be aware of the resolution their timing calls provide. Because it is on-chip, shared memory is much faster than local and global memory.
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