In such cases, call cudaGetDeviceProperties() to determine whether the device is capable of a certain feature. This Best Practices Guide is a manual to help developers obtain the best performance from NVIDIA CUDA GPUs. In some instances, operations performed by automatic NUMA balancing may degrade the performance of applications running on NVIDIA GPUs. This chapter discusses how to correctly measure performance using CPU timers and CUDA events. A stream is simply a sequence of operations that are performed in order on the device. For other applications, the problem size will grow to fill the available processors. If A, B, and C are floating-point values, (A+B)+C is not guaranteed to equal A+(B+C) as it is in symbolic math. 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. (tens of kBs capacity) Global memory is main memory (GDDR,HBM, (1-32 GB)) and data is cached by L2,L1 caches. In Overlapping computation and data transfers, the memory copy and kernel execution occur sequentially. Although the CUDA Runtime provides the option of static linking, some libraries included in the CUDA Toolkit are available only in dynamically-linked form. You must declare a single extern unsized array as before, and use pointers into it to divide it into multiple arrays, as in the following excerpt. 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. 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. The results of these calculations can frequently differ from pure 64-bit operations performed on the CUDA device. A threads execution can only proceed past a __syncthreads() after all threads in its block have executed the __syncthreads(). These transfers are costly in terms of performance and should be minimized. 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. Hence, its important to design your application to use threads and blocks in a way that maximizes hardware utilization and to limit practices that impede the free distribution of work. 32/48/64/96/128K depending on the GPU and current configuration) and each block can use a chunk of it by declaring shared memory. Concurrent copy and execute illustrates the basic technique. Page-locked or pinned memory transfers attain the highest bandwidth between the host and the device. Code that uses the warp shuffle operation, for example, must be compiled with -arch=sm_30 (or higher compute capability). See Registers for details. An application that exhibits linear strong scaling has a speedup equal to the number of processors used. The CUDA runtime has relaxed the minimum driver version check and thus no longer requires a driver upgrade when moving to a new minor release. Medium Priority: To hide latency arising from register dependencies, maintain sufficient numbers of active threads per multiprocessor (i.e., sufficient occupancy). In order to optimize the performance, when the size of the persistent data is more than the size of the set-aside L2 cache portion, we tune the num_bytes and hitRatio parameters in the access window as below. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. This is particularly beneficial to kernels that frequently call __syncthreads(). It is therefore best to redistribute the CUDA Runtime library with the application when using dynamic linking or else to statically link against the CUDA Runtime. On the other hand, some applications designs will require some amount of refactoring to expose their inherent parallelism. The kernel is executed within a loop in host code that varies the parameter offset from 0 to 32. A copy kernel that illustrates misaligned accesses. For devices of compute capability 2.x, there are two settings, 48KBshared memory / 16KB L1 cache, and 16KB shared memory / 48KB L1 cache. Strong Scaling and Amdahls Law describes strong scaling, which allows us to set an upper bound for the speedup with a fixed problem size. 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. 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. After each round of application parallelization is complete, the developer can move to optimizing the implementation to improve performance. Shared memory is a powerful feature for writing well-optimized CUDA code. By leveraging the semantic versioning, starting with CUDA 11, components in the CUDA Toolkit will remain binary compatible across the minor versions of the toolkit. When using multiple GPUs from the same application, it is recommended to use GPUs of the same type, rather than mixing hardware generations. Prior to CUDA 11.0, the minimum driver version for a toolkit was the same as the driver shipped with that version of the CUDA Toolkit. THIS DOCUMENT AND ALL NVIDIA DESIGN SPECIFICATIONS, REFERENCE BOARDS, FILES, DRAWINGS, DIAGNOSTICS, LISTS, AND OTHER DOCUMENTS (TOGETHER AND SEPARATELY, MATERIALS) ARE BEING PROVIDED AS IS. NVIDIA MAKES NO WARRANTIES, EXPRESSED, IMPLIED, STATUTORY, OR OTHERWISE WITH RESPECT TO THE MATERIALS, AND EXPRESSLY DISCLAIMS ALL IMPLIED WARRANTIES OF NONINFRINGEMENT, MERCHANTABILITY, AND FITNESS FOR A PARTICULAR PURPOSE. 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. 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. Register dependencies arise when an instruction uses a result stored in a register written by an instruction before it. 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. C++-style convenience wrappers (cuda_runtime.h) built on top of the C-style functions. Just-in-time compilation increases application load time but allows applications to benefit from latest compiler improvements. Improvement by reading additional data into shared memory. Other peculiarities of floating-point arithmetic are presented in Features and Technical Specifications of the CUDA C++ Programming Guide as well as in a whitepaper and accompanying webinar on floating-point precision and performance available from https://developer.nvidia.com/content/precision-performance-floating-point-and-ieee-754-compliance-nvidia-gpus. Using Kolmogorov complexity to measure difficulty of problems? When you parallelize computations, you potentially change the order of operations and therefore the parallel results might not match sequential results. Similarly, the single-precision functions sinpif(), cospif(), and sincospif() should replace calls to sinf(), cosf(), and sincosf() when the function argument is of the form *. 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. Shared memory is a powerful feature for writing well optimized CUDA code. This is done by the nvcc compiler when it determines that there is insufficient register space to hold the variable. The host runtime component of the CUDA software environment can be used only by host functions. The reciprocal square root should always be invoked explicitly as rsqrtf() for single precision and rsqrt() for double precision. The access policy window requires a value for hitRatio and num_bytes. In Unoptimized handling of strided accesses to global memory, the row-th, col-th element of C is obtained by taking the dot product of the row-th and col-th rows of A. The list of active processes running on the GPU is reported, along with the corresponding process name/ID and allocated GPU memory. 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). Shared memory is allocated per thread block, so all threads in the block have access to the same shared memory. It is recommended to use cudaMallocAsync() and cudaFreeAsync() which are stream ordered pool allocators to manage device memory. The throughput of __sinf(x), __cosf(x), and__expf(x) is much greater than that of sinf(x), cosf(x), and expf(x). The NVIDIA Ampere GPU architecture allows CUDA users to control the persistence of data in L2 cache. As a result, this section discusses size but not dimension. Floating Point Math Is not Associative, 8.2.3. This does not apply to the NVIDIA Driver; the end user must still download and install an NVIDIA Driver appropriate to their GPU(s) and operating system. The details of managing the accelerator device are handled implicitly by an OpenACC-enabled compiler and runtime. Also of note is the Thrust library, which is a parallel C++ template library similar to the C++ Standard Template Library. The goal is to maximize the use of the hardware by maximizing bandwidth. 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. 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. Starting with CUDA 11, the toolkit versions are based on an industry-standard semantic versioning scheme: .X.Y.Z, where: .X stands for the major version - APIs have changed and binary compatibility is broken. These memory spaces include global, local, shared, texture, and registers, as shown in Figure 2. So while the impact is still evident it is not as large as we might have expected. A minimum of 64 threads per block should be used, and only if there are multiple concurrent blocks per multiprocessor. The following example illustrates the basic technique. An example would be modeling how two molecules interact with each other, where the molecule sizes are fixed. 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. Detecting Hardware and Software Configuration. --ptxas-options=-v or -Xptxas=-v lists per-kernel register, shared, and constant memory usage. A Sequential but Misaligned Access Pattern, 9.2.2.2. This situation is not different from what is available today where developers use macros to compile out features based on CUDA versions. Replace sin(*) with sinpi(), cos(*) with cospi(), and sincos(*) with sincospi(). Shared Memory in Matrix Multiplication (C=AB), 9.2.3.3. Low Priority: Make it easy for the compiler to use branch predication in lieu of loops or control statements. Starting with the Volta architecture, Independent Thread Scheduling allows a warp to remain diverged outside of the data-dependent conditional block. For example, on a device of compute capability 7.0, a kernel with 128-thread blocks using 37 registers per thread results in an occupancy of 75% with 12 active 128-thread blocks per multi-processor, whereas a kernel with 320-thread blocks using the same 37 registers per thread results in an occupancy of 63% because only four 320-thread blocks can reside on a multiprocessor. For this reason, ensuring that as much as possible of the data in each cache line fetched is actually used is an important part of performance optimization of memory accesses on these devices. GPUs with compute capability 8.6 support shared memory capacity of 0, 8, 16, 32, 64 or 100 KB per SM. The one exception here is when multiple threads in a warp address the same shared memory location, resulting in a broadcast. On integrated GPUs (i.e., GPUs with the integrated field of the CUDA device properties structure set to 1), mapped pinned memory is always a performance gain because it avoids superfluous copies as integrated GPU and CPU memory are physically the same. Cached in L1 and L2 by default except on devices of compute capability 5.x; devices of compute capability 5.x cache locals only in L2. However, compared to cache based architectures, like CPUs, latency hiding architectures, like GPUs, tend to cope better with completely random memory access patterns. A C-style function interface (cuda_runtime_api.h). Per thread resources required by a CUDA kernel might limit the maximum block size in an unwanted way. The context is explicit in the CUDA Driver API but is entirely implicit in the CUDA Runtime API, which creates and manages contexts automatically. Thedriver will honor the specified preference except when a kernel requires more shared memory per thread block than available in the specified configuration. Choosing execution parameters is a matter of striking a balance between latency hiding (occupancy) and resource utilization. For portability, that is, to be able to execute code on future GPU architectures with higher compute capability (for which no binary code can be generated yet), an application must load PTX code that will be just-in-time compiled by the NVIDIA driver for these future devices. Production code should, however, systematically check the error code returned by each API call and check for failures in kernel launches by calling cudaGetLastError(). Please refer to the EULA for details. Unified memory: supports seamless access to buffers or objects from multiple GPUs and CPUs. By comparison, the smallest executable unit of parallelism on a CUDA device comprises 32 threads (termed a warp of threads). 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 versions of the components in the toolkit are available in this table. Because it is on-chip, shared memory is much faster than local and global memory. It provides functions to handle the following: Interoperability with OpenGL and Direct3D. Two types of runtime math operations are supported. Verify that your library doesnt leak dependencies, breakages, namespaces, etc. It is best to enable this option in most circumstances. Figure 4 corresponds to this misalignments) The effective bandwidth for the copy with various offsets on an NVIDIA Tesla V100 (compute capability 7.0) is shown in Figure 5. See Math Libraries. For example, improving occupancy from 66 percent to 100 percent generally does not translate to a similar increase in performance. The same goes for other CUDA Toolkit libraries: cuFFT has an interface similar to that of FFTW, etc. In this particular example, the offset memory throughput achieved is, however, approximately 9/10th, because adjacent warps reuse the cache lines their neighbors fetched. Here cudaEventRecord() is used to place the start and stop events into the default stream, stream 0. No contractual obligations are formed either directly or indirectly by this document. 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. CUDA Memory Global Memory We used global memory to hold the functions values. HBM2 memories, on the other hand, provide dedicated ECC resources, allowing overhead-free ECC protection.2. APIs can be deprecated and removed. Overall Performance Optimization Strategies, https://developer.nvidia.com/nsight-visual-studio-edition, https://developer.nvidia.com/debugging-solutions, https://developer.nvidia.com/content/precision-performance-floating-point-and-ieee-754-compliance-nvidia-gpus, Asynchronous and Overlapping Transfers with Computation, CUDA Driver API :: CUDA Toolkit Documentation, dynamically-linked version of the CUDA Runtime library, Where to Install Redistributed CUDA Libraries, https://developer.nvidia.com/gpu-deployment-kit, https://developer.nvidia.com/nvidia-management-library-nvml, https://developer.nvidia.com/cluster-management. This means that even though an application source might need to be changed if it has to be recompiled against a newer CUDA Toolkit in order to use the newer features, replacing the driver components installed in a system with a newer version will always support existing applications and its functions. The hitRatio parameter can be used to specify the fraction of accesses that receive the hitProp property. 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). A place where magic is studied and practiced? Minimize redundant accesses to global memory whenever possible. Tuning the Access Window Hit-Ratio, 9.2.3.2. For Windows 8, SetDefaultDLLDirectories() and AddDllDirectory() should be used instead of SetDllDirectory(). This document is not a commitment to develop, release, or deliver any Material (defined below), code, or functionality. CUDA 11.0 introduces an async-copy feature that can be used within device code to explicitly manage the asynchronous copying of data from global memory to shared memory. This should be our first candidate function for parallelization. Armed with this knowledge, the developer can evaluate these bottlenecks for parallelization and start to investigate GPU acceleration. (In Staged concurrent copy and execute, it is assumed that N is evenly divisible by nThreads*nStreams.) The application should also maximize parallel execution at a higher level by explicitly exposing concurrent execution on the device through streams, as well as maximizing concurrent execution between the host and the device. To learn more, see our tips on writing great answers. For the preceding procedure, assuming matrices of size NxN, there are N2 operations (additions) and 3N2 elements transferred, so the ratio of operations to elements transferred is 1:3 or O(1). CUDA Compatibility Developers Guide, 15.3.1. The performance of the sliding-window benchmark with tuned hit-ratio. For example, if you link against the CUDA 11.1 dynamic runtime, and use functionality from 11.1, as well as a separate shared library that was linked against the CUDA 11.2 dynamic runtime that requires 11.2 functionality, the final link step must include a CUDA 11.2 or newer dynamic runtime. Computing a row of a tile in C using one row of A and an entire tile of B.. Although each of these instructions is scheduled for execution, only the instructions with a true predicate are actually executed. Many of the industrys most popular cluster management tools support CUDA GPUs via NVML. The SONAME of the library against which the application was built must match the filename of the library that is redistributed with the application. In our use case, BLOCK_SIZE + 2 * RADIUS = $1024 + 2 \times 6000$ = $13024$ and the size of an int is $4$ Byte, therefore, the shared memory required is $17024 \times 4 / 1024$ = $50.875$ KB, which is larger than the maximum static shared memory we could have. Strong Scaling and Amdahls Law, 3.1.3.2. CUDA Toolkit is released on a monthly release cadence to deliver new features, performance improvements, and critical bug fixes. The CUDA Toolkit libraries (cuBLAS, cuFFT, etc.) Memory optimizations are the most important area for performance. The first is the compute capability, and the second is the version number of the CUDA Runtime and CUDA Driver APIs. Register storage enables threads to keep local variables nearby for low-latency access. Threads with a false predicate do not write results, and also do not evaluate addresses or read operands. The cudaGetDeviceCount() function can be used to query for the number of available devices. However, bank conflicts occur when copying the tile from global memory into shared memory. Data should be kept on the device as long as possible. Therefore, in terms of wxw tiles, A is a column matrix, B is a row matrix, and C is their outer product; see Figure 11. Because of these nuances in register allocation and the fact that a multiprocessors shared memory is also partitioned between resident thread blocks, the exact relationship between register usage and occupancy can be difficult to determine. Copy the results from device memory to host memory, also called device-to-host transfer. CUDA programming involves running code on two different platforms concurrently: a host system with one or more CPUs and one or more CUDA-enabled NVIDIA GPU devices. CUDA work occurs within a process space for a particular GPU known as a context. The achieved bandwidth is approximately 790 GB/s. Because kernel1 and kernel2 are executed in different, non-default streams, a capable device can execute the kernels at the same time. 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. libcuda.so on Linux systems). Not the answer you're looking for? Threads on a CPU are generally heavyweight entities. I have locally sorted queues in different blocks of cuda. 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. This guide refers to and relies on several other documents that you should have at your disposal for reference, all of which are available at no cost from the CUDA website https://docs.nvidia.com/cuda/. 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. It will not allow any other CUDA call to begin until it has completed.) The following sections explain the principal items of interest. The maximum number of thread blocks per SM is 32 for devices of compute capability 8.0 (i.e., A100 GPUs) and 16 for GPUs with compute capability 8.6. If no new features are used (or if they are used conditionally with fallbacks provided) youll be able to remain compatible. For devices with compute capability of 2.0 or greater, the Visual Profiler can be used to collect several different memory throughput measures. For a warp of threads, col represents sequential columns of the transpose of A, and therefore col*TILE_DIM represents a strided access of global memory with a stride of w, resulting in plenty of wasted bandwidth.
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