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Starting with the Volta architecture, Independent Thread Scheduling allows a warp to remain diverged outside of the data-dependent conditional block. Because the driver may interleave execution of CUDA calls from other non-default streams, calls in other streams may be included in the timing. High Priority: Avoid different execution paths within the same warp. Shared Memory in Matrix Multiplication (C=AB), 9.2.3.3. ? Misaligned sequential addresses that fall within five 32-byte segments, Memory allocated through the CUDA Runtime API, such as via cudaMalloc(), is guaranteed to be aligned to at least 256 bytes. However, it is best to avoid accessing global memory whenever possible. Strong Scaling and Amdahls Law describes strong scaling, which allows us to set an upper bound for the speedup with a fixed problem size. NVRTC used to support only virtual architectures through the option -arch, since it was only emitting PTX. PTX defines a virtual machine and ISA for general purpose parallel thread execution. A minimum of 64 threads per block should be used, and only if there are multiple concurrent blocks per multiprocessor. For exponentiation using base 2 or 10, use the functions exp2() or expf2() and exp10() or expf10() rather than the functions pow() or powf(). See Building for Maximum Compatibility for further discussion of the flags used for building code for multiple generations of CUDA-capable device simultaneously. Sequential copy and execute and Staged concurrent copy and execute demonstrate this. Since shared memory is shared amongst threads in a thread block, it provides a mechanism for threads to cooperate. Note that in Improvement by reading additional data into shared memory, a __syncthreads() call is required after reading the B tile because a warp reads data from shared memory that were written to shared memory by different warps. The formulas in the table below are valid for x >= 0, x != -0, that is, signbit(x) == 0. SPWorley April 15, 2011, 6:13pm #3 If you really need to save per-block information from dynamic shared memory between kernel launchess, you could allocate global memory, equal to the block count times the dynamic shared size. If not, my suggestion would be to start by breaking your work into separate kernels, and using the kernel launch(es) as sync points. A device in which work is poorly balanced across the multiprocessors will deliver suboptimal performance. When using the driver APIs directly, we recommend using the new driver entry point access API (cuGetProcAddress) documented here: CUDA Driver API :: CUDA Toolkit Documentation. Devices of compute capability 2.0 and later support a special addressing mode called Unified Virtual Addressing (UVA) on 64-bit Linux and Windows. If the shared memory array size is known at compile time, as in the staticReverse kernel, then we can explicitly declare an array of that size, as we do with the array s. In this kernel, t and tr are the two indices representing the original and reverse order, respectively. The SONAME of the library against which the application was built must match the filename of the library that is redistributed with the application. A pointer to a structure with a size embedded is a better solution. The maximum number of concurrent warps per SM remains the same as in Volta (i.e., 64), and other factors influencing warp occupancy are: The register file size is 64K 32-bit registers per SM. 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. This microbenchmark uses a 1024 MB region in GPU global memory. The CUDA Toolkit libraries (cuBLAS, cuFFT, etc.) The NVIDIA Ampere GPU architecture allows CUDA users to control the persistence of data in L2 cache. If no new features are used (or if they are used conditionally with fallbacks provided) youll be able to remain compatible. An Efficient Matrix Transpose in CUDA C/C++, How to Access Global Memory Efficiently in CUDA C/C++ Kernels, How to Access Global Memory Efficiently in CUDA Fortran Kernels, Top Video Streaming and Conferencing Sessions at NVIDIA GTC 2023, Top Cybersecurity Sessions at NVIDIA GTC 2023, Top Conversational AI Sessions at NVIDIA GTC 2023, Top AI Video Analytics Sessions at NVIDIA GTC 2023, Top Data Science Sessions at NVIDIA GTC 2023. We want to ensure that each change we make is correct and that it improves performance (and by how much). The NVIDIA System Management Interface (nvidia-smi) is a command line utility that aids in the management and monitoring of NVIDIA GPU devices. We can see this usage in the following example: NVRTC is a runtime compilation library for CUDA C++. Support for Bfloat16 Tensor Core, through HMMA instructions. The following table presents the evolution of matrix instruction sizes and supported data types for Tensor Cores across different GPU architecture generations. The current board power draw and power limits are reported for products that report these measurements. These bindings expose the same features as the C-based interface and also provide backwards compatibility. Shared memory can be thought of as a software-controlled cache on the processor - each Streaming Multiprocessor has a small amount of shared memory (e.g. Detecting Hardware and Software Configuration. This is done with the FLDCW x86 assembly instruction or the equivalent operating system API. For purposes of calculating occupancy, the number of registers used by each thread is one of the key factors. The hardware splits a conflicting memory request into as many separate conflict-free requests as necessary, decreasing the effective bandwidth by a factor equal to the number of colliding memory requests. Thanks for contributing an answer to Stack Overflow! As with the previous section on library building recommendations, if using the CUDA runtime, we recommend linking to the CUDA runtime statically when building your application. Improvement by reading additional data into shared memory. 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. Where to Install Redistributed CUDA Libraries, 17.4. This approach should be used even if one of the steps in a sequence of calculations could be performed faster on the host. Checking these things frequently as an integral part of our cyclical APOD process will help ensure that we achieve the desired results as rapidly as possible. The results of the various optimizations are summarized in Table 2. The NVIDIA Ampere GPU architecture adds hardware acceleration for copying data from global memory to shared memory. It allows developers to use a CUDA-enabled graphics processing unit (GPU) to accelerate processing tasks in their applications. Please note that new versions of nvidia-smi are not guaranteed to be backward-compatible with previous versions. For Windows 8, SetDefaultDLLDirectories() and AddDllDirectory() should be used instead of SetDllDirectory(). CUDA supports several compatibility choices: First introduced in CUDA 10, the CUDA Forward Compatible Upgrade is designed to allow users to get access to new CUDA features and run applications built with new CUDA releases on systems with older installations of the NVIDIA datacenter driver. Each generation of CUDA-capable device has an associated compute capability version that indicates the feature set supported by the device (see CUDA Compute Capability). Low Priority: Use shift operations to avoid expensive division and modulo calculations. Along with the increased capacity, the bandwidth of the L2 cache to the SMs is also increased. Both correctable single-bit and detectable double-bit errors are reported. 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. The following sections discuss some caveats and considerations. A lower occupancy kernel will have more registers available per thread than a higher occupancy kernel, which may result in less register spilling to local memory; in particular, with a high degree of exposed instruction-level parallelism (ILP) it is, in some cases, possible to fully cover latency with a low occupancy. The maximum number of registers per thread is 255. To allocate an array in shared memory we . For devices with compute capability of 2.0 or greater, the Visual Profiler can be used to collect several different memory throughput measures. Weak scaling is a measure of how the time to solution changes as more processors are added to a system with a fixed problem size per processor; i.e., where the overall problem size increases as the number of processors is increased. The other three kernels in this example use dynamically allocated shared memory, which can be used when the amount of shared memory is not known at compile time. Throughput Reported by Visual Profiler, 9.1. Reading from a texture while writing to its underlying global memory array in the same kernel launch should be avoided because the texture caches are read-only and are not invalidated when the associated global memory is modified. However, a few rules of thumb should be followed: Threads per block should be a multiple of warp size to avoid wasting computation on under-populated warps and to facilitate coalescing. This is shown in Figure 1. Indicate whether the NVIDIA driver stays loaded when no applications are connected to the GPU. NVIDIA Ampere GPU Architecture Tuning Guide, 1.4. A noteworthy exception to this are completely random memory access patterns. In fact, local memory is off-chip. The NVIDIA A100 GPU increases the HBM2 memory capacity from 32 GB in V100 GPU to 40 GB in A100 GPU. The criteria of benefit and scope for establishing priority will vary depending on the nature of the program. Awareness of how instructions are executed often permits low-level optimizations that can be useful, especially in code that is run frequently (the so-called hot spot in a program). Shared Memory. With the CUDA Driver API, a CUDA application process can potentially create more than one context for a given GPU. 11.x). Higher compute capability versions are supersets of lower (that is, earlier) versions, so they are backward compatible. 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. An application has no direct control over these bank conflicts. As the host and device memories are separated, items in the host memory must occasionally be communicated between device memory and host memory as described in What Runs on a CUDA-Enabled Device?. Avoid long sequences of diverged execution by threads within the same warp. This suggests trading precision for speed when it does not affect the end result, such as using intrinsics instead of regular functions or single precision instead of double precision. Always check the error return values on all CUDA API functions, even for functions that are not expected to fail, as this will allow the application to detect and recover from errors as soon as possible should they occur. CUDA driver - User-mode driver component used to run CUDA applications (e.g. For example, a matrix multiplication of the same matrices requires N3 operations (multiply-add), so the ratio of operations to elements transferred is O(N), in which case the larger the matrix the greater the performance benefit. This optimization is especially important for global memory accesses, because latency of access costs hundreds of clock cycles. It accepts CUDA C++ source code in character string form and creates handles that can be used to obtain the PTX. Therefore, the total number of links available is increased to twelve in A100, versus six in V100, yielding 600 GB/s bidirectional bandwidth versus 300 GB/s for V100. Because of this, the maximum speedup S of a program is: Another way of looking at Gustafsons Law is that it is not the problem size that remains constant as we scale up the system but rather the execution time. Please see the MSDN documentation for these routines for more information. Customer should obtain the latest relevant information before placing orders and should verify that such information is current and complete. Concurrent copy and execute illustrates the basic technique. A CUDA device has a number of different memory components that are available to programmers - register, shared memory, local memory, global memory and constant memory. Medium Priority: The number of threads per block should be a multiple of 32 threads, because this provides optimal computing efficiency and facilitates coalescing. Reinitialize the GPU hardware and software state via a secondary bus reset. Note that the timings are measured on the GPU clock, so the timing resolution is operating-system-independent. "After the incident", I started to be more careful not to trip over things. In particular, developers should note the number of multiprocessors on the device, the number of registers and the amount of memory available, and any special capabilities of the device. For example, a 64-bit application linked to cuBLAS 5.5 will look for cublas64_55.dll at runtime, so this is the file that should be redistributed with that application, even though cublas.lib is the file that the application is linked against. Shared memory is a CUDA memory space that is shared by all threads in a thread block. In the kernel launch, specify the total shared memory needed, as in the following. Even though such an access requires only 1 transaction on devices of compute capability 2.0 or higher, there is wasted bandwidth in the transaction, because only one 4-byte word out of 8 words in a 32-byte cache segment is used. Therefore, an application that compiled successfully on an older version of the toolkit may require changes in order to compile against a newer version of the toolkit. I'm not sure if this will fit your overall processing. Actions that present substantial improvements for most CUDA applications have the highest priority, while small optimizations that affect only very specific situations are given a lower priority. Copyright 2020-2023, NVIDIA Corporation & Affiliates. NVIDIA products are sold subject to the NVIDIA standard terms and conditions of sale supplied at the time of order acknowledgement, unless otherwise agreed in an individual sales agreement signed by authorized representatives of NVIDIA and customer (Terms of Sale). Last updated on Feb 27, 2023. cudaFuncAttributePreferredSharedMemoryCarveout, 1. Using a profiler, the developer can identify such hotspots and start to compile a list of candidates for parallelization. As a result, it is recommended that first-time readers proceed through the guide sequentially. Memory instructions include any instruction that reads from or writes to shared, local, or global memory. TF32 is a new 19-bit Tensor Core format that can be easily integrated into programs for more accurate DL training than 16-bit HMMA formats. No. Staged concurrent copy and execute shows how the transfer and kernel execution can be broken up into nStreams stages. If it has not, subsequent compilation phases might still decide otherwise, if they find the variable consumes too much register space for the targeted architecture. The cudaDeviceCanAccessPeer() can be used to determine if peer access is possible between any pair of GPUs. How to time code using CUDA events illustrates their use. Users should refer to the CUDA headers and documentation for new CUDA APIs introduced in a release. 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. Both the CUDA driver and the CUDA runtime are not source compatible across the different SDK releases. Memory optimizations are the most important area for performance. The host system and the device each have their own distinct attached physical memories 1. No license, either expressed or implied, is granted under any NVIDIA patent right, copyright, or other NVIDIA intellectual property right under this document. See the nvidia-smi documenation for details. Throughout this guide, Kepler refers to devices of compute capability 3.x, Maxwell refers to devices of compute capability 5.x, Pascal refers to device of compute capability 6.x, Volta refers to devices of compute capability 7.0, Turing refers to devices of compute capability 7.5, and NVIDIA Ampere GPU Architecture refers to devices of compute capability 8.x. We will note some of them later on in the document. For codes continuing to make use of PTX, in order to support compiling on an older driver, your code must be first transformed into device code via the static ptxjitcompiler library or NVRTC with the option of generating code for a specific architecture (e.g. If sequential threads in a warp access memory that is sequential but not aligned with a 32-byte segment, five 32-byte segments will be requested, as shown in Figure 4. 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. Therefore, to accurately measure the elapsed time for a particular call or sequence of CUDA calls, it is necessary to synchronize the CPU thread with the GPU by calling cudaDeviceSynchronize() immediately before starting and stopping the CPU timer. In the next post I will continue our discussion of shared memory by using it to optimize a matrix transpose. In fact, shared memory latency is roughly 100x lower than uncached global memory latency (provided that there are no bank conflicts between the threads, which we will examine later in this post). In Using shared memory to improve the global memory load efficiency in matrix multiplication, each element in a tile of A is read from global memory only once, in a fully coalesced fashion (with no wasted bandwidth), to shared memory. The Perl bindings are provided via CPAN and the Python bindings via PyPI. Information published by NVIDIA regarding third-party products or services does not constitute a license from NVIDIA to use such products or services or a warranty or endorsement thereof. \left( 0.877 \times 10^{9} \right. It will now support actual architectures as well to emit SASS. 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). The latency of most arithmetic instructions is typically 4 cycles on devices of compute capability 7.0. A shared memory request for a warp is split into one request for the first half of the warp and one request for the second half of the warp. Going a step further, if most functions are defined as __host__ __device__ rather than just __device__ functions, then these functions can be tested on both the CPU and the GPU, thereby increasing our confidence that the function is correct and that there will not be any unexpected differences in the results. This chapter examines issues that can affect the correctness of returned data and points to appropriate solutions. 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. 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. To achieve high memory bandwidth for concurrent accesses, shared memory is divided into equally sized memory modules (banks) that can be accessed simultaneously. Data Transfer Between Host and Device provides further details, including the measurements of bandwidth between the host and the device versus within the device proper. This document is not a commitment to develop, release, or deliver any Material (defined below), code, or functionality. Last updated on Feb 27, 2023. Hence, the A100 GPU enables a single thread block to address up to 163 KB of shared memory and GPUs with compute capability 8.6 can address up to 99 KB of shared memory in a single thread block. If all threads of a warp access the same location, then constant memory can be as fast as a register access. Because the minimum memory transaction size is larger than most word sizes, the actual memory throughput required for a kernel can include the transfer of data not used by the kernel. The context is explicit in the CUDA Driver API but is entirely implicit in the CUDA Runtime API, which creates and manages contexts automatically. In CUDA only threads and the host can access memory. Hence, access to local memory is as expensive as access to global memory. The third generation NVLink has the same bi-directional data rate of 50 GB/s per link, but uses half the number of signal pairs to achieve this bandwidth. This advantage is increased when several powers of the same base are needed (e.g., where both x2 and x5 are calculated in close proximity), as this aids the compiler in its common sub-expression elimination (CSE) optimization. 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. These copy instructions are asynchronous, with respect to computation and allow users to explicitly control overlap of compute with data movement from global memory into the SM. Data Transfer Between Host and Device, 9.1.2. As for optimizing instruction usage, the use of arithmetic instructions that have low throughput should be avoided. Although the CUDA Runtime provides the option of static linking, some libraries included in the CUDA Toolkit are available only in dynamically-linked form. How to manage this resource utilization is discussed in the final sections of this chapter. 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. A variant of the previous matrix multiplication can be used to illustrate how strided accesses to global memory, as well as shared memory bank conflicts, are handled. To use other CUDA APIs introduced in a minor release (that require a new driver), one would have to implement fallbacks or fail gracefully. The results are shown in the chart below, where we see good performance regardless of whether the persistent data fits in the L2 set-aside or not. The issue here is the number of operations performed per data element transferred. So, when an application is built with CUDA 11.0, it can only run on a system with an R450 or later driver. When the persistent data region fits well into the 30 MB set-aside portion of the L2 cache, a performance increase of as much as 50% is observed. One method for doing so utilizes shared memory, which is discussed in the next section. Therefore, it is important to be sure to compare values of like precision and to express the results within a certain tolerance rather than expecting them to be exact. As a particular example, to evaluate the sine function in degrees instead of radians, use sinpi(x/180.0). Block-column matrix multiplied by block-row matrix. CUDA applications are built against the CUDA Runtime library, which handles device, memory, and kernel management. Page-locked memory mapping is enabled by calling cudaSetDeviceFlags() with cudaDeviceMapHost. However, this requires writing to shared memory in columns, and because of the use of wxw tiles in shared memory, this results in a stride between threads of w banks - every thread of the warp hits the same bank (Recall that w is selected as 32). NVIDIA Corporation (NVIDIA) makes no representations or warranties, expressed or implied, as to the accuracy or completeness of the information contained in this document and assumes no responsibility for any errors contained herein. Computing a row of a tile. For optimal performance, users should manually tune the NUMA characteristics of their application. For more information please refer to the section on Async Copy in the CUDA C++ Programming Guide. Like Volta, the NVIDIA Ampere GPU architecture combines the functionality of the L1 and texture caches into a unified L1/Texture cache which acts as a coalescing buffer for memory accesses, gathering up the data requested by the threads of a warp prior to delivery of that data to the warp. While the details of how to apply these strategies to a particular application is a complex and problem-specific topic, the general themes listed here apply regardless of whether we are parallelizing code to run on for multicore CPUs or for use on CUDA GPUs.