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. (See Data Transfer Between Host and Device.) The host code in Zero-copy host code shows how zero copy is typically set up. 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. Between 128 and 256 threads per block is a good initial range for experimentation with different block sizes. To target specific versions of NVIDIA hardware and CUDA software, use the -arch, -code, and -gencode options of nvcc. A very important performance consideration in programming for CUDA-capable GPU architectures is the coalescing of global memory accesses. 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. For recent versions of CUDA hardware, misaligned data accesses are not a big issue. 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. In other words, a cubin object generated for compute capability X.y will only execute on devices of compute capability X.z where zy. The async-copy does not require the copy_count parameter to be a multiple of 4, to maximize performance through compiler optimizations. likewise return their own sets of error codes. Asynchronous and Overlapping Transfers with Computation, 9.2.1.2. 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. Results obtained using double-precision arithmetic will frequently differ from the same operation performed via single-precision arithmetic due to the greater precision of the former and due to rounding issues. We want to ensure that each change we make is correct and that it improves performance (and by how much). 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. Functions following functionName() naming convention are slower but have higher accuracy (e.g., sinf(x) and expf(x)). Conversely, if P is a small number (meaning that the application is not substantially parallelizable), increasing the number of processors N does little to improve performance. The functions that make up the CUDA Runtime API are explained in the CUDA Toolkit Reference Manual. Constantly recompiling with the latest CUDA Toolkit means forcing upgrades on the end-customers of an application product. The NVIDIA Ampere GPU architecture includes new Third Generation Tensor Cores that are more powerful than the Tensor Cores used in Volta and Turing SMs. Other company and product names may be trademarks of the respective companies with which they are associated. The difference is in how threads in a half warp access elements of A in the second term, a[col*TILE_DIM+i], for each iteration i. The number of elements is multiplied by the size of each element (4 bytes for a float), multiplied by 2 (because of the read and write), divided by 109 (or 1,0243) to obtain GB of memory transferred. The primary differences are in threading model and in separate physical memories: Execution pipelines on host systems can support a limited number of concurrent threads. For more information on this pragma, refer to the CUDA C++ Programming Guide. Sometimes, the best optimization might even be to avoid any data transfer in the first place by simply recomputing the data whenever it is needed. The larger N is(that is, the greater the number of processors), the smaller the P/N fraction. 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. 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. 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. Can anyone please tell me how to do these two operations? This is shown in Figure 1. With UVA, the host memory and the device memories of all installed supported devices share a single virtual address space. Another common approach to parallelization of sequential codes is to make use of parallelizing compilers. 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. 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. 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. It is however usually more effective to use a high-level programming language such as C++. The following throughput metrics can be displayed in the Details or Detail Graphs view: The Requested Global Load Throughput and Requested Global Store Throughput values indicate the global memory throughput requested by the kernel and therefore correspond to the effective bandwidth obtained by the calculation shown under Effective Bandwidth Calculation. Excessive use can reduce overall system performance because pinned memory is a scarce resource, but how much is too much is difficult to know in advance. To minimize bank conflicts, it is important to understand how memory addresses map to memory banks. This allows applications that depend on these libraries to redistribute the exact versions of the libraries against which they were built and tested, thereby avoiding any trouble for end users who might have a different version of the CUDA Toolkit (or perhaps none at all) installed on their machines. 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. Global memory loads and stores by threads of a warp are coalesced by the device into as few as possible transactions. The number of registers available, the maximum number of simultaneous threads resident on each multiprocessor, and the register allocation granularity vary over different compute capabilities. These examples assume compute capability 6.0 or higher and that accesses are for 4-byte words, unless otherwise noted. (To determine the latter number, see the deviceQuery CUDA Sample or refer to Compute Capabilities in the CUDA C++ Programming Guide.) 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. An example would be modeling how two molecules interact with each other, where the molecule sizes are fixed. We cannot declare these directly, but small static allocations go . 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.
cuda-c-best-practices-guide 12.1 documentation - NVIDIA Developer Then, thread A wants to read Bs element from shared memory, and vice versa. Minimize redundant accesses to global memory whenever possible. All CUDA compute devices follow the IEEE 754 standard for binary floating-point representation, with some small exceptions. In particular, a larger block size does not imply a higher occupancy. When a CUDA kernel accesses a data region in the global memory repeatedly, such data accesses can be considered to be persisting. This is a requirement for good performance on CUDA: the software must use a large number (generally thousands or tens of thousands) of concurrent threads. 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. NVIDIA makes no representation or warranty that products based on this document will be suitable for any specified use. outside your established ABI contract. 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. Load the GPU program and execute, caching data on-chip for performance. aims to make the expression of this parallelism as simple as possible, while simultaneously enabling operation on CUDA-capable GPUs designed for maximum parallel throughput. // Type of access property on cache miss. Shared memory is specified by the device architecture and is measured on per-block basis. 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. The CUDA compiler (nvcc), provides a way to handle CUDA and non-CUDA code (by splitting and steering compilation), along with the CUDA runtime, is part of the CUDA compiler toolchain. Strong Scaling and Amdahls Law, 3.1.3.2. As PTX is compiled by the CUDA driver, new toolchains will generate PTX that is not compatible with the older CUDA driver. Although it is also possible to synchronize the CPU thread with a particular stream or event on the GPU, these synchronization functions are not suitable for timing code in streams other than the default stream. This information is obtained by calling cudaGetDeviceProperties() and accessing the information in the structure it returns. As such, the constant cache is best when threads in the same warp accesses only a few distinct locations. An application has no direct control over these bank conflicts. //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. For GPUs with compute capability 8.6 maximum shared memory per thread block is 99 KB. In contrast with cudaMemcpy(), the asynchronous transfer version requires pinned host memory (see Pinned Memory), and it contains an additional argument, a stream ID. Shared Memory in Matrix Multiplication (C=AB), 9.2.3.3. Figure 6 illustrates such a situation; in this case, threads within a warp access words in memory with a stride of 2. Hardware Acceleration for Split Arrive/Wait Barrier, 1.4.1.4. A stride of 2 results in a 50% of load/store efficiency since half the elements in the transaction are not used and represent wasted bandwidth. For example, the compiler may use predication to avoid an actual branch. Applications compiled against a CUDA Toolkit version will only run on systems with the specified minimum driver version for that toolkit version. This section examines the functionality, advantages, and pitfalls of both approaches. Within each iteration of the for loop, a value in shared memory is broadcast to all threads in a warp. This number is divided by the time in seconds to obtain GB/s. The kernel is executed within a loop in host code that varies the parameter offset from 0 to 32. Now that we are working block by block, we should use shared memory. The CUDA Toolkit Samples provide several helper functions for error checking with the various CUDA APIs; these helper functions are located in the samples/common/inc/helper_cuda.h file in the CUDA Toolkit. Lets say that two threads A and B each load a data element from global memory and store it to shared memory. Just-in-time compilation increases application load time but allows applications to benefit from latest compiler improvements. Registers are allocated to an entire block all at once. 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. Furthermore, register allocations are rounded up to the nearest 256 registers per warp. In such a case, the bandwidth would be 836.4 GiB/s. Shared memory can also be used to avoid uncoalesced memory accesses by loading and storing data in a coalesced pattern from global memory and then reordering it in shared memory. High Priority: To maximize developer productivity, profile the application to determine hotspots and bottlenecks. 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. Warp level support for Reduction Operations, 1.4.2.1. 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. We can see this usage in the following example: NVRTC is a runtime compilation library for CUDA C++. Code that uses the warp shuffle operation, for example, must be compiled with -arch=sm_30 (or higher compute capability). Let's say that there are m blocks. 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). Staging Ground Beta 1 Recap, and Reviewers needed for Beta 2, Atomic operations on Shared Memory in CUDA. NVIDIA shall have no liability for the consequences or use of such information or for any infringement of patents or other rights of third parties that may result from its use. By comparison, the smallest executable unit of parallelism on a CUDA device comprises 32 threads (termed a warp of threads). Occupancy is the ratio of the number of active warps per multiprocessor to the maximum number of possible active warps. Applications compiled with CUDA toolkit versions as old as 3.2 will run on newer drivers.