Shared Memory. Upgrading dependencies is error-prone and time consuming, and in some corner cases, can even change the semantics of a program. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. In particular, there is no register-related reason to pack data into vector data types such as float4 or int4 types. See Registers for details. If instead i is declared as signed, where the overflow semantics are undefined, the compiler has more leeway to use these optimizations. On the other hand, some applications designs will require some amount of refactoring to expose their inherent parallelism. Reproduction of information in this document is permissible only if approved in advance by NVIDIA in writing, reproduced without alteration and in full compliance with all applicable export laws and regulations, and accompanied by all associated conditions, limitations, and notices. Assess, Parallelize, Optimize, Deploy, 3.1.3.1. The first and simplest case of coalescing can be achieved by any CUDA-enabled device of compute capability 6.0 or higher: the k-th thread accesses the k-th word in a 32-byte aligned array. In this guide, they represent a typical case. The following documents are especially important resources: In particular, the optimization section of this guide assumes that you have already successfully downloaded and installed the CUDA Toolkit (if not, please refer to the relevant CUDA Installation Guide for your platform) and that you have a basic familiarity with the CUDA C++ programming language and environment (if not, please refer to the CUDA C++ Programming Guide). Note that when a thread block allocates more registers than are available on a multiprocessor, the kernel launch fails, as it will when too much shared memory or too many threads are requested. Performance Improvements Optimizing C = AB Matrix Multiply
The following example is based on gprof, which is an open-source profiler for Linux platforms from the GNU Binutils collection. 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. See Hardware Multithreading of the CUDA C++ Programming Guide for the register allocation formulas for devices of various compute capabilities and Features and Technical Specifications of the CUDA C++ Programming Guide for the total number of registers available on those devices. Randomly accessing. A CUDA context is a software environment that manages memory and other resources New APIs can be added in minor versions. Details about occupancy are displayed in the Occupancy section. Automatic variables that are likely to be placed in local memory are large structures or arrays that would consume too much register space and arrays that the compiler determines may be indexed dynamically. Medium Priority: Use the fast math library whenever speed trumps precision. For most purposes, the key point is that the larger the parallelizable portion P is, the greater the potential speedup. For example, if the install name of the cuBLAS library is given as @rpath/libcublas.5.5.dylib, then the library is version 5.5 and the copy of this library redistributed with the application must be named libcublas.5.5.dylib, even though only -lcublas (with no version number specified) is used at link time. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide, How Intuit democratizes AI development across teams through reusability. Both the CUDA driver and the CUDA runtime are not source compatible across the different SDK releases. I think this pretty much implies that you are going to have the place the heads of each queue in global memory. Even though each multiprocessor contains thousands of 32-bit registers (see Features and Technical Specifications of the CUDA C++ Programming Guide), these are partitioned among concurrent threads. 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. To measure performance accurately, it is useful to calculate theoretical and effective bandwidth. In such a case, the bandwidth would be 836.4 GiB/s. Recommendations for building a minor-version compatible library, 15.4.1.5. The cudaEventElapsedTime() function returns the time elapsed between the recording of the start and stop events. Refer to the CUDA Toolkit Release Notes for details for the minimum driver version and the version of the driver shipped with the toolkit. A noteworthy exception to this are completely random memory access patterns. So threads must wait approximatly 4 cycles before using an arithmetic result. The nature of simulating nature: A Q&A with IBM Quantum researcher Dr. Jamie We've added a "Necessary cookies only" option to the cookie consent popup. High Priority: Ensure global memory accesses are coalesced whenever possible. The throughput of __sinf(x), __cosf(x), and__expf(x) is much greater than that of sinf(x), cosf(x), and expf(x). NVRTC used to support only virtual architectures through the option -arch, since it was only emitting PTX. In this section, we will review the usage patterns that may require new user workflows when taking advantage of the compatibility features of the CUDA platform. See https://developer.nvidia.com/nvidia-management-library-nvml for additional information. UVA is also a necessary precondition for enabling peer-to-peer (P2P) transfer of data directly across the PCIe bus or NVLink for supported GPUs in supported configurations, bypassing host memory. Theoretical bandwidth can be calculated using hardware specifications available in the product literature. It is important to use the same divisor when calculating theoretical and effective bandwidth so that the comparison is valid. How to manage this resource utilization is discussed in the final sections of this chapter. While the contents can be used as a reference manual, you should be aware that some topics are revisited in different contexts as various programming and configuration topics are explored. This situation is not different from what is available today where developers use macros to compile out features based on CUDA versions. 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. There is a total of 64 KB constant memory on a device. For this purpose, it requires mapped pinned (non-pageable) memory. Detecting Hardware and Software Configuration. By understanding the end-users requirements and constraints and by applying Amdahls and Gustafsons laws, the developer can determine the upper bound of performance improvement from acceleration of the identified portions of the application. NVIDIA accepts no liability related to any default, damage, costs, or problem which may be based on or attributable to: (i) the use of the NVIDIA product in any manner that is contrary to this document or (ii) customer product designs. However we now add the underlying driver to that mix. While multiple contexts (and their associated resources such as global memory allocations) can be allocated concurrently on a given GPU, only one of these contexts can execute work at any given moment on that GPU; contexts sharing the same GPU are time-sliced. Tuning the Access Window Hit-Ratio, 9.2.3.2. On discrete GPUs, mapped pinned memory is advantageous only in certain cases. Fast, low-precision interpolation between texels, Valid only if the texture reference returns floating-point data, Can be used only with normalized texture coordinates, 1 The automatic handling of boundary cases in the bottom row of Table 4 refers to how a texture coordinate is resolved when it falls outside the valid addressing range. Using a profiler, the developer can identify such hotspots and start to compile a list of candidates for parallelization. You want to sort all the queues before you collect them. As seen above, in the case of misaligned sequential accesses, caches help to alleviate the performance impact. Before addressing specific performance tuning issues covered in this guide, refer to the NVIDIA Ampere GPU Architecture Compatibility Guide for CUDA Applications to ensure that your application is compiled in a way that is compatible with the NVIDIA Ampere GPU Architecture. In the NVIDIA Ampere GPU architecture, the portion of the L1 cache dedicated to shared memory (known as the carveout) can be selected at runtime as in previous architectures such as Volta, using cudaFuncSetAttribute() with the attribute cudaFuncAttributePreferredSharedMemoryCarveout. CUDA Toolkit Library Redistribution, 16.4.1.2. Improvement by reading additional data into shared memory. ? One of several factors that determine occupancy is register availability. For example, improving occupancy from 66 percent to 100 percent generally does not translate to a similar increase in performance. Warp level support for Reduction Operations, 1.4.2.1. Transfers between NVLink-connected endpoints are automatically routed through NVLink, rather than PCIe. The functions that make up the CUDA Runtime API are explained in the CUDA Toolkit Reference Manual. For this workflow, a new nvptxcompiler_static library is shipped with the CUDA Toolkit. One method for doing so utilizes shared memory, which is discussed in the next section. All threads within one block see the same shared memory array . Support for TF32 Tensor Core, through HMMA instructions. Copyright 2020-2023, NVIDIA Corporation & Affiliates. Global memory loads and stores by threads of a warp are coalesced by the device into as few as possible transactions. Its like a local cache shared among the threads of a block. To minimize bank conflicts, it is important to understand how memory addresses map to memory banks and how to optimally schedule memory requests. These barriers can also be used alongside the asynchronous copy. But this technique is still useful for other access patterns, as Ill show in the next post.). On parallel systems, it is possible to run into difficulties not typically found in traditional serial-oriented programming. The hardware splits a memory request that has bank conflicts into as many separate conflict-free requests as necessary, decreasing the effective bandwidth by a factor equal to the number of separate memory requests. In the code in Zero-copy host code, kernel() can reference the mapped pinned host memory using the pointer a_map in exactly the same was as it would if a_map referred to a location in device memory. 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). The CUDA Toolkits End-User License Agreement (EULA) allows for redistribution of many of the CUDA libraries under certain terms and conditions. See Register Pressure. Using shared memory to improve the global memory load efficiency in matrix multiplication. The major and minor revision numbers of the compute capability are shown on the seventh line of Figure 16. Non-default streams (streams other than stream 0) are required for concurrent execution because 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. The discussions in this guide all use the C++ programming language, so you should be comfortable reading C++ code. From supercomputers to mobile phones, modern processors increasingly rely on parallelism to provide performance. Managing your GPU cluster will help achieve maximum GPU utilization and help you and your users extract the best possible performance. All CUDA threads can access it for read and write. Last updated on Feb 27, 2023. cudaFuncAttributePreferredSharedMemoryCarveout, 1. Copyright 2007-2023, NVIDIA Corporation & Affiliates. Performance benefits can be more readily achieved when this ratio is higher. Built on top of these technologies are CUDA libraries, some of which are included in the CUDA Toolkit, while others such as cuDNN may be released independently of the CUDA Toolkit. To use other CUDA APIs introduced in a minor release (that require a new driver), one would have to implement fallbacks or fail gracefully. // Number of bytes for persisting accesses. outside your established ABI contract. Likewise, for exponentation with an exponent of -1/3, use rcbrt() or rcbrtf(). For example, if the threads of a warp access adjacent 4-byte words (e.g., adjacent float values), four coalesced 32-byte transactions will service that memory access. While compiler optimization improvements continually seek to narrow this gap, explicit multiplication (or the use of an equivalent purpose-built inline function or macro) can have a significant advantage. When redistributing the dynamically-linked versions of one or more CUDA libraries, it is important to identify the exact files that need to be redistributed. Understanding Scaling discusses the potential benefit we might expect from such parallelization. Texture memory is also designed for streaming fetches with a constant latency; that is, a cache hit reduces DRAM bandwidth demand, but not fetch latency. So, if each thread block uses many registers, the number of thread blocks that can be resident on a multiprocessor is reduced, thereby lowering the occupancy of the multiprocessor. Production code should, however, systematically check the error code returned by each API call and check for failures in kernel launches by calling cudaGetLastError(). Error counts are provided for both the current boot cycle and the lifetime of the GPU. nvidia-smi ships with NVIDIA GPU display drivers on Linux, and with 64-bit Windows Server 2008 R2 and Windows 7. nvidia-smi can output queried information as XML or as human-readable plain text either to standard output or to a file. My code is GPL licensed, can I issue a license to have my code be distributed in a specific MIT licensed project? This helps in reducing cache thrashing. When choosing the first execution configuration parameter-the number of blocks per grid, or grid size - the primary concern is keeping the entire GPU busy. We define source compatibility as a set of guarantees provided by the library, where a well-formed application built against a specific version of the library (using the SDK) will continue to build and run without errors when a newer version of the SDK is installed. Even a relatively slow kernel may be advantageous if it avoids one or more transfers between host and device memory. For Windows 8, SetDefaultDLLDirectories() and AddDllDirectory() should be used instead of SetDllDirectory(). These include threading issues, unexpected values due to the way floating-point values are computed, and challenges arising from differences in the way CPU and GPU processors operate. CUDA kernel and thread hierarchy Devices of compute capability 3.x have configurable bank size, which can be set using cudaDeviceSetSharedMemConfig() to either four bytes (cudaSharedMemBankSizeFourByte, thedefault) or eight bytes (cudaSharedMemBankSizeEightByte). In this case the shared memory allocation size per thread block must be specified (in bytes) using an optional third execution configuration parameter, as in the following excerpt. On Windows, if the CUDA Runtime or other dynamically-linked CUDA Toolkit library is placed in the same directory as the executable, Windows will locate it automatically. Since some CUDA API calls and all kernel launches are asynchronous with respect to the host code, errors may be reported to the host asynchronously as well; often this occurs the next time the host and device synchronize with each other, such as during a call to cudaMemcpy() or to cudaDeviceSynchronize(). However, it is best to avoid accessing global memory whenever possible. However, as with APOD as a whole, program optimization is an iterative process (identify an opportunity for optimization, apply and test the optimization, verify the speedup achieved, and repeat), meaning that it is not necessary for a programmer to spend large amounts of time memorizing the bulk of all possible optimization strategies prior to seeing good speedups. Two types of runtime math operations are supported. Use of such information may require a license from a third party under the patents or other intellectual property rights of the third party, or a license from NVIDIA under the patents or other intellectual property rights of NVIDIA. The hitRatio parameter can be used to specify the fraction of accesses that receive the hitProp property. Because the size of the persistent memory region (freqSize * sizeof(int) bytes) is much, smaller than the size of the streaming memory region (dataSize * sizeof(int) bytes), data, in the persistent region is accessed more frequently*/, //Number of bytes for persisting accesses in range 10-60 MB, //Hint for cache hit ratio. An example is transposing [1209, 9] of any type and 32 tile size. Finally, higher bandwidth between the host and the device is achieved when using page-locked (or pinned) memory, as discussed in the CUDA C++ Programming Guide and the Pinned Memory section of this document. Fixed value 1.0, The performance of the sliding-window benchmark with fixed hit-ratio of 1.0. Note that the performance improvement is not due to improved coalescing in either case, but to avoiding redundant transfers from global memory. To execute code on devices of specific compute capability, an application must load binary or PTX code that is compatible with this compute capability. 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. In such cases, kernels with 32x32 or 64x16 threads can be launched with each thread processing four elements of the shared memory array. For more information on the Runtime API, refer to CUDA Runtime of the CUDA C++ Programming Guide. For GPUs with compute capability 8.6 maximum shared memory per thread block is 99 KB. See the CUDA C++ Programming Guide for details. For example, cuMemMap APIs or any of APIs introduced prior to CUDA 11.0, such as cudaDeviceSynchronize, do not require a driver upgrade. Using unrealistic workloads can lead to sub-optimal results and wasted effort both by causing developers to optimize for unrealistic problem sizes and by causing developers to concentrate on the wrong functions. Best performance with synchronous copy is achieved when the copy_count parameter is a multiple of 4 for all three element sizes. The reciprocal square root should always be invoked explicitly as rsqrtf() for single precision and rsqrt() for double precision. Medium Priority: Use shared memory to avoid redundant transfers from global memory. To analyze performance, it is necessary to consider how warps access global memory in the for loop. When an application will be deployed to target machines of arbitrary/unknown configuration, the application should explicitly test for the existence of a CUDA-capable GPU in order to take appropriate action when no such device is available. There are many such factors involved in selecting block size, and inevitably some experimentation is required. If L1-caching is enabled on these devices, the number of required transactions is equal to the number of required 128-byte aligned segments. When using CPU timers, it is critical to remember that many CUDA API functions are asynchronous; that is, they return control back to the calling CPU thread prior to completing their work. 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. Missing dependencies is also a binary compatibility break, hence you should provide fallbacks or guards for functionality that depends on those interfaces. Whether a device has this capability is indicated by the concurrentKernels field of the cudaDeviceProp structure (or listed in the output of the deviceQuery CUDA Sample). One way to use shared memory that leverages such thread cooperation is to enable global memory coalescing, as demonstrated by the array reversal in this post. Accesses to the remaining data of the memory region (i.e., streaming data) are considered normal or streaming accesses and will thus use the remaining 10 MB of the non set-aside L2 portion (unless part of the L2 set-aside portion is unused). Modern NVIDIA GPUs can support up to 2048 active threads concurrently per multiprocessor (see Features and Specifications of the CUDA C++ Programming Guide) On GPUs with 80 multiprocessors, this leads to more than 160,000 concurrently active threads.
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