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. Staged concurrent copy and execute shows how the transfer and kernel execution can be broken up into nStreams stages. 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. The following issues should be considered when determining what parts of an application to run on the device: The device is ideally suited for computations that can be run on numerous data elements simultaneously in parallel. It may be different with non-unit-strided accesses, however, and this is a pattern that occurs frequently when dealing with multidimensional data or matrices. CUDA Toolkit and Minimum Driver Versions. To execute any CUDA program, there are three main steps: Copy the input data from host memory to device memory, also known as host-to-device transfer. 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. Performance benefits can be more readily achieved when this ratio is higher. 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. All threads within one block see the same shared memory array . This is an aggressive optimization that can both reduce numerical accuracy and alter special case handling. The results of the various optimizations are summarized in Table 2. Consequently, the order in which arithmetic operations are performed is important. The NVIDIA Ampere GPU architecture allows CUDA users to control the persistence of data in L2 cache. Now I have some problems. 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. For example, to use only devices 0 and 2 from the system-wide list of devices, set CUDA_VISIBLE_DEVICES=0,2 before launching the application. 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. 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. PTX programs are translated at load time to the target hardware instruction set via the JIT Compiler which is part of the CUDA driver. 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. This capability (combined with thread synchronization) has a number of uses, such as user-managed data caches, high-performance cooperative parallel algorithms (parallel reductions, for example), and tofacilitate global memory coalescing in cases where it would otherwise not be possible. 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. On the other hand, some applications designs will require some amount of refactoring to expose their inherent parallelism. Notwithstanding any damages that customer might incur for any reason whatsoever, NVIDIAs aggregate and cumulative liability towards customer for the products described herein shall be limited in accordance with the Terms of Sale for the product. With the use of shared memory we can fetch data from global memory and place it into on-chip memory with far lower latency and higher bandwidth then global memory. The application will then enumerate these devices as device 0 and device 1, respectively. Then with a tile size of 32, the shared memory buffer will be of shape [32, 32]. To get a closer match between values, set the x86 host processor to use regular double or single precision (64 bits and 32 bits, respectively). With each generation of NVIDIA processors, new features are added to the GPU that CUDA can leverage. Using shared memory to improve the global memory load efficiency in matrix multiplication. No license, either expressed or implied, is granted under any NVIDIA patent right, copyright, or other NVIDIA intellectual property right under this document. For more information on the Runtime API, refer to CUDA Runtime of the CUDA C++ Programming Guide. Each threadblock would do the work it needs to (e.g. As PTX is compiled by the CUDA driver, new toolchains will generate PTX that is not compatible with the older CUDA driver. This is shown in Figure 1. 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 is the default if using nvcc to link in CUDA 5.5 and later. But since any repeated access to such memory areas causes repeated CPU-GPU transfers, consider creating a second area in device memory to manually cache the previously read host memory data. However, the SONAME of this library is given as libcublas.so.5.5: Because of this, even if -lcublas (with no version number specified) is used when linking the application, the SONAME found at link time implies that libcublas.so.5.5 is the name of the file that the dynamic loader will look for when loading the application and therefore must be the name of the file (or a symlink to the same) that is redistributed with the application. For certain devices of compute capability 5.2, L1-caching of accesses to global memory can be optionally enabled. .Z stands for the release/patch version - new updates and patches will increment this. Timeline comparison for copy and kernel execution, Table 1. 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. Shared memory is extremely fast, user managed, on-chip memory that can be used to share data between threads within a thread block. Device memory allocation and de-allocation via cudaMalloc() and cudaFree() are expensive operations. The throughput of individual arithmetic operations is detailed in the CUDA C++ Programming Guide. Having identified the hotspots and having done the basic exercises to set goals and expectations, the developer needs to parallelize the code. Mutually exclusive execution using std::atomic? The key here is that libraries are most useful when they match well with the needs of the application. 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. It is recommended to use cudaMallocAsync() and cudaFreeAsync() which are stream ordered pool allocators to manage device memory. 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. Then, thread A wants to read Bs element from shared memory, and vice versa. Not using intermediate registers can help reduce register pressure and can increase kernel occupancy. The PTX string generated by NVRTC can be loaded by cuModuleLoadData and cuModuleLoadDataEx. The compiler must on occasion insert conversion instructions, introducing additional execution cycles. 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. This is the case for: Functions operating on char or short whose operands generally need to be converted to an int, Double-precision floating-point constants (defined without any type suffix) used as input to single-precision floating-point computations. To do so, use this equation: \(\text{Effective\ bandwidth} = \left( {\left( B_{r} + B_{w} \right) \div 10^{9}} \right) \div \text{time}\). Low Priority: Make it easy for the compiler to use branch predication in lieu of loops or control statements. Resources stay allocated to each thread until it completes its execution. 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. 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. Consider a simple transpose of a [2048, 1024] matrix to [1024, 2048]. These examples assume compute capability 6.0 or higher and that accesses are for 4-byte words, unless otherwise noted. Intermediate data structures should be created in device memory, operated on by the device, and destroyed without ever being mapped by the host or copied to host memory. However, this latency can be completely hidden by the execution of threads in other warps. 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. More information on cubins, PTX and application compatibility can be found in the CUDA C++ Programming Guide. This is common for building applications that are GPU architecture, platform and compiler agnostic. 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. NVLink operates transparently within the existing CUDA model. 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. To specify an alternate path where the libraries will be distributed, use linker options similar to those below: For Linux and Mac, the -rpath option is used as before. Applications that do not check for CUDA API errors could at times run to completion without having noticed that the data calculated by the GPU is incomplete, invalid, or uninitialized. NVIDIA makes no representation or warranty that products based on this document will be suitable for any specified use. 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. Device 0 of this system has compute capability 7.0. For example, many kernels have complex addressing logic for accessing memory in addition to their actual computation. 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. Each version of the CUDA Toolkit (and runtime) requires a minimum version of the NVIDIA driver. This ensures your code is compatible. Applications using the new API can load the final device code directly using driver APIs cuModuleLoadData and cuModuleLoadDataEx. Because the memory copy and the kernel both return control to the host immediately, the host function cpuFunction() overlaps their execution. For more information on the Arrive/Wait Barriers refer to the Arrive/Wait Barrier section in the CUDA C++ Programming Guide. (Note that on devices of Compute Capability 1.2 or later, the memory system can fully coalesce even the reversed index stores to global memory. Strong scaling is usually equated with Amdahls Law, which specifies the maximum speedup that can be expected by parallelizing portions of a serial program. In such cases, and when the execution time (tE) exceeds the transfer time (tT), a rough estimate for the overall time is tE + tT/nStreams for the staged version versus tE + tT for the sequential version. Although the CUDA Runtime provides the option of static linking, some libraries included in the CUDA Toolkit are available only in dynamically-linked form. Understanding which type of scaling is most applicable to an application is an important part of estimating speedup. Now that we are working block by block, we should use shared memory. Concurrent copy and execute demonstrates how to overlap kernel execution with asynchronous data transfer. 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. Any CPU timer can be used to measure the elapsed time of a CUDA call or kernel execution. However, the set of registers (known as the register file) is a limited commodity that all threads resident on a multiprocessor must share. In order to maintain forward compatibility to future hardware and toolkits and to ensure that at least one thread block can run on an SM, developers should include the single argument __launch_bounds__(maxThreadsPerBlock) which specifies the largest block size that the kernel will be launched with. For global memory accesses, this comparison of requested memory bandwidth to actual memory bandwidth is reported by the Global Memory Load Efficiency and Global Memory Store Efficiency metrics. Formulae for exponentiation by small fractions, Sample CUDA configuration data reported by deviceQuery, +-----------------------------------------------------------------------------+, |-------------------------------+----------------------+----------------------+, |===============================+======================+======================|, +-------------------------------+----------------------+----------------------+, |=============================================================================|, cudaDevAttrCanUseHostPointerForRegisteredMem, 1.3. However, it is possible to coalesce memory access in such cases if we use shared memory. An explicit __syncwarp() can be used to guarantee that the warp has reconverged for subsequent instructions. Static linking makes the executable slightly larger, but it ensures that the correct version of runtime library functions are included in the application binary without requiring separate redistribution of the CUDA Runtime library. In this guide, they represent a typical case. This access pattern results in four 32-byte transactions, indicated by the red rectangles.
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