It would have been more so if adjacent warps had not exhibited such a high degree of reuse of the over-fetched cache lines. Some metric related to the number of active warps on a multiprocessor is therefore important in determining how effectively the hardware is kept busy. The actual memory throughput shows how close the code is to the hardware limit, and a comparison of the effective or requested bandwidth to the actual bandwidth presents a good estimate of how much bandwidth is wasted by suboptimal coalescing of memory accesses (see Coalesced Access to Global Memory). This spreadsheet, shown in Figure 15, is called CUDA_Occupancy_Calculator.xls and is located in the tools subdirectory of the CUDA Toolkit installation. 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). See the CUDA C++ Programming Guide for further explanations and software requirements for UVA and P2P. 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. Note this switch is effective only on single-precision floating point. This means that in one of these devices, for a multiprocessor to have 100% occupancy, each thread can use at most 32 registers. Data copied from global memory to shared memory using asynchronous copy instructions can be cached in the L1 cache or the L1 cache can be optionally bypassed. Optimal global memory coalescing is achieved for both reads and writes because global memory is always accessed through the linear, aligned index t. The reversed index tr is only used to access shared memory, which does not have the sequential access restrictions of global memory for optimal performance. The goal is to maximize the use of the hardware by maximizing bandwidth. compute_80). Therefore, it is best to avoid multiple contexts per GPU within the same CUDA application. [DRAFT][CUDA][Schedule] Better Layout Transform Schedules by Developers are notified through deprecation and documentation mechanisms of any current or upcoming changes. As described in Memory Optimizations of this guide, bandwidth can be dramatically affected by the choice of memory in which data is stored, how the data is laid out and the order in which it is accessed, as well as other factors. Threads can access data in shared memory loaded from global memory by other threads within the same thread block. 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. When our CUDA 11.1 application (i.e. Performance Improvements Optimizing C = AB Matrix Multiply The ldd tool is useful for identifying the exact filenames of the libraries that the application expects to find at runtime as well as the path, if any, of the copy of that library that the dynamic loader would select when loading the application given the current library search path: In a shared library on Mac OS X, there is a field called the install name that indicates the expected installation path and filename the library; the CUDA libraries also use this filename to indicate binary compatibility. This is shown in Figure 1. Using Kolmogorov complexity to measure difficulty of problems? Furthermore, there should be multiple active blocks per multiprocessor so that blocks that arent waiting for a __syncthreads() can keep the hardware busy. The following example illustrates the basic technique. An application that exhibits linear strong scaling has a speedup equal to the number of processors used. The latter case can be avoided by using single-precision floating-point constants, defined with an f suffix such as 3.141592653589793f, 1.0f, 0.5f. The reason shared memory is used in this example is to facilitate global memory coalescing on older CUDA devices (Compute Capability 1.1 or earlier). 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. For example, transferring two matrices to the device to perform a matrix addition and then transferring the results back to the host will not realize much performance benefit. Strong scaling is usually equated with Amdahls Law, which specifies the maximum speedup that can be expected by parallelizing portions of a serial program. This chapter examines issues that can affect the correctness of returned data and points to appropriate solutions. Does there exist a square root of Euler-Lagrange equations of a field? For example, many kernels have complex addressing logic for accessing memory in addition to their actual computation. The performance of the above kernel is shown in the chart below. The only performance issue with shared memory is bank conflicts, which we will discuss later. High Priority: Minimize data transfer between the host and the device, even if it means running some kernels on the device that do not show performance gains when compared with running them on the host CPU. The CUDA Driver API thus is binary-compatible (the OS loader can pick up a newer version and the application continues to work) but not source-compatible (rebuilding your application against a newer SDK might require source changes). Hence, access to local memory is as expensive as access to global memory. By using new CUDA versions, users can benefit from new CUDA programming model APIs, compiler optimizations and math library features. CUDA shared memory of other blocks - Stack Overflow Within each iteration of the for loop, a value in shared memory is broadcast to all threads in a warp. //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. The performance of the kernels is shown in Figure 14. A copy kernel that illustrates misaligned accesses. NVIDIA-SMI can be used to configure a GPU for exclusive process mode, which limits the number of contexts per GPU to one. This technique could be used when the data dependency is such that the data can be broken into chunks and transferred in multiple stages, launching multiple kernels to operate on each chunk as it arrives. One of several factors that determine occupancy is register availability. Medium Priority: To hide latency arising from register dependencies, maintain sufficient numbers of active threads per multiprocessor (i.e., sufficient occupancy). Consequently, the order in which arithmetic operations are performed is important. For regions of system memory that have already been pre-allocated, cudaHostRegister() can be used to pin the memory on-the-fly without the need to allocate a separate buffer and copy the data into it. Note that no bank conflict occurs if only one memory location per bank is accessed by a half warp of threads. To use dynamic linking with the CUDA Runtime when using the nvcc from CUDA 5.5 or later to link the application, add the --cudart=shared flag to the link command line; otherwise the statically-linked CUDA Runtime library is used by default. In order to profit from any modern processor architecture, GPUs included, the first steps are to assess the application to identify the hotspots, determine whether they can be parallelized, and understand the relevant workloads both now and in the future. 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. How to time code using CUDA events illustrates their use. On Wednesday, February 19, 2020, NVIDIA will present part 2 of a 9-part CUDA Training Series titled "CUDA Shared Memory". It can be copied into the same directory as the application executable or into a subdirectory of that installation path. Furthermore, register allocations are rounded up to the nearest 256 registers per warp. Use several smaller thread blocks rather than one large thread block per multiprocessor if latency affects performance. 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. Not using intermediate registers can help reduce register pressure and can increase kernel occupancy. Fetching ECC bits for each memory transaction also reduced the effective bandwidth by approximately 20% compared to the same GPU with ECC disabled, though the exact impact of ECC on bandwidth can be higher and depends on the memory access pattern. Distributing the CUDA Runtime and Libraries, 16.4.1. GPUs with compute capability 8.6 support shared memory capacity of 0, 8, 16, 32, 64 or 100 KB per SM. If the PTX is also not available, then the kernel launch will fail. Since you don't indicate where your "locally sorted" data resides, this could indicate a copying of that much data at least (for example, if they are locally sorted and reside in shared memory). Block-column matrix multiplied by block-row matrix. 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. In such cases, kernels with 32x32 or 64x16 threads can be launched with each thread processing four elements of the shared memory array. Shared memory Bank Conflicts: Shared memory bank conflicts exist and are common for the strategy used. 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. It is customers sole responsibility to evaluate and determine the applicability of any information contained in this document, ensure the product is suitable and fit for the application planned by customer, and perform the necessary testing for the application in order to avoid a default of the application or the product. The remaining portion of this persistent data will be accessed using the streaming property. (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.). These transfers are costly in terms of performance and should be minimized. Kernels can be written using the CUDA instruction set architecture, called PTX, which is described in the PTX reference manual. In this calculation, the memory clock rate is converted in to Hz, multiplied by the interface width (divided by 8, to convert bits to bytes) and multiplied by 2 due to the double data rate. Sometimes, the compiler may unroll loops or optimize out if or switch statements by using branch predication instead. 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. Maximizing parallel execution starts with structuring the algorithm in a way that exposes as much parallelism as possible. 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. By understanding how applications can scale it is possible to set expectations and plan an incremental parallelization strategy. The same goes for other CUDA Toolkit libraries: cuFFT has an interface similar to that of FFTW, etc. 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. On devices of compute capability 6.0 or higher, L1-caching is the default, however the data access unit is 32-byte regardless of whether global loads are cached in L1 or not. The following sections discuss some caveats and considerations. To allocate an array in shared memory we . For this example, it is assumed that the data transfer and kernel execution times are comparable. By simply increasing this parameter (without modifying the kernel), it is possible to effectively reduce the occupancy of the kernel and measure its effect on performance. Code samples throughout the guide omit error checking for conciseness. Instead, all instructions are scheduled, but a per-thread condition code or predicate controls which threads execute the instructions. To minimize bank conflicts, it is important to understand how memory addresses map to memory banks. Minimize redundant accesses to global memory whenever possible. Incorrect or unexpected results arise principally from issues of floating-point accuracy due to the way floating-point values are computed and stored. In CUDA there is no defined global synchronization mechanism except the kernel launch. cuda shared memory and block execution scheduling 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. As an example, the assignment operator in the following sample code has a high throughput, but, crucially, there is a latency of hundreds of clock cycles to read data from global memory: Much of this global memory latency can be hidden by the thread scheduler if there are sufficient independent arithmetic instructions that can be issued while waiting for the global memory access to complete. It is possible to rearrange the collection of installed CUDA devices that will be visible to and enumerated by a CUDA application prior to the start of that application by way of the CUDA_VISIBLE_DEVICES environment variable. By describing your computation in terms of these high-level abstractions you provide Thrust with the freedom to select the most efficient implementation automatically. There are many such factors involved in selecting block size, and inevitably some experimentation is required. High Priority: Use the effective bandwidth of your computation as a metric when measuring performance and optimization benefits. 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).
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