The cudaGetDeviceProperties() function reports various features of the available devices, including the CUDA Compute Capability of the device (see also the Compute Capabilities section of the CUDA C++ Programming Guide). For example, cuMemMap APIs or any of APIs introduced prior to CUDA 11.0, such as cudaDeviceSynchronize, do not require a driver upgrade. For example, in the standard CUDA Toolkit installation, the files libcublas.so and libcublas.so.5.5 are both symlinks pointing to a specific build of cuBLAS, which is named like libcublas.so.5.5.x, where x is the build number (e.g., libcublas.so.5.5.17). Before we proceed further on this topic, its important for developers to understand the concept of Minimum Driver Version and how that may affect them. When statically linking to the CUDA Runtime, multiple versions of the runtime can peacably coexist in the same application process simultaneously; for example, if an application uses one version of the CUDA Runtime, and a plugin to that application is statically linked to a different version, that is perfectly acceptable, as long as the installed NVIDIA Driver is sufficient for both. Using shared memory to coalesce global reads. Not all threads need to participate. These examples assume compute capability 6.0 or higher and that accesses are for 4-byte words, unless otherwise noted. For example, it may be desirable to use a 64x64 element shared memory array in a kernel, but because the maximum number of threads per block is 1024, it is not possible to launch a kernel with 64x64 threads per block. An optimized handling of strided accesses using coalesced reads from global memory. On the other hand, some applications designs will require some amount of refactoring to expose their inherent parallelism. CUDA provides a simple barrier synchronization primitive, __syncthreads(). In general, they should be avoided, because compared to peak capabilities any architecture processes these memory access patterns at a low efficiency. All threads within one block see the same shared memory array . The synchronous version for the kernel loads an element from global memory to an intermediate register and then stores the intermediate register value to shared memory. 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. Instead, all instructions are scheduled, but a per-thread condition code or predicate controls which threads execute the instructions. Threads copy the data from global memory to shared memory with the statement s[t] = d[t], and the reversal is done two lines later with the statement d[t] = s[tr]. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. cudaOccupancyMaxActiveBlocksPerMultiprocessor, to dynamically select launch configurations based on runtime parameters. The example below shows how an existing example can be adapted to use the new features, guarded by the USE_CUBIN macro in this case: We recommend that the CUDA runtime be statically linked to minimize dependencies. Customer should obtain the latest relevant information before placing orders and should verify that such information is current and complete. In short, CPU cores are designed to minimize latency for a small number of threads at a time each, whereas GPUs are designed to handle a large number of concurrent, lightweight threads in order to maximize throughput. In this case, multiple broadcasts from different banks are coalesced into a single multicast from the requested shared memory locations to the threads. The kernel also uses the default stream, and it will not begin execution until the memory copy completes; therefore, no explicit synchronization is needed. So threads must wait approximatly 4 cycles before using an arithmetic result. Max and current clock rates are reported for several important clock domains, as well as the current GPU performance state (pstate). Another, more aggressive, option is -use_fast_math, which coerces every functionName() call to the equivalent __functionName() call. 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. However, this approach of determining how register count affects occupancy does not take into account the register allocation granularity. The dynamic shared memory kernel, dynamicReverse(), declares the shared memory array using an unsized extern array syntax, extern shared int s[] (note the empty brackets and use of the extern specifier). Block-column matrix multiplied by block-row matrix. The CUDA driver ensures backward Binary Compatibility is maintained for compiled CUDA applications. An exception is the case where all threads in a warp address the same shared memory address, resulting in a broadcast. The cudaDeviceEnablePeerAccess() API call remains necessary to enable direct transfers (over either PCIe or NVLink) between GPUs. For further details on the programming features discussed in this guide, please refer to the CUDA C++ Programming Guide. So while the impact is still evident it is not as large as we might have expected. Non-default streams are required for this overlap because memory copy, memory set functions, and 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. APIs can be deprecated and removed. Higher occupancy does not always equate to higher performance-there is a point above which additional occupancy does not improve performance. In the NVIDIA Ampere GPU architecture remote NVLINK accesses go through a Link TLB on the remote GPU. Finally, this product is divided by 109 to convert the result to GB/s. (Developers targeting a single machine with known configuration may choose to skip this section.). 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. As described in Asynchronous and Overlapping Transfers with Computation, CUDA streams can be used to overlap kernel execution with data transfers. The programmer can also control loop unrolling using. Support for Bfloat16 Tensor Core, through HMMA instructions. 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. By default the 48KBshared memory setting is used. 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). Upgrading dependencies is error-prone and time consuming, and in some corner cases, can even change the semantics of a program. The list of active processes running on the GPU is reported, along with the corresponding process name/ID and allocated GPU memory. In a typical system, thousands of threads are queued up for work (in warps of 32 threads each). 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. For more details on the new Tensor Core operations refer to the Warp Matrix Multiply section in the CUDA C++ Programming Guide. The NVIDIA Ampere GPU architecture adds native support for warp wide reduction operations for 32-bit signed and unsigned integer operands. For more information on the Arrive/Wait Barriers refer to the Arrive/Wait Barrier section in the CUDA C++ Programming Guide. Because it is on-chip, shared memory has much higher bandwidth and lower latency than local and global memory - provided there are no bank conflicts between the threads, as detailed in the following section. For other algorithms, implementations may be considered correct if they match the reference within some small epsilon. Fixed value 1.0, The performance of the sliding-window benchmark with fixed hit-ratio of 1.0. However, the set of registers (known as the register file) is a limited commodity that all threads resident on a multiprocessor must share. These accept one of three options:cudaFuncCachePreferNone, cudaFuncCachePreferShared, and cudaFuncCachePreferL1. To prevent the compiler from allocating too many registers, use the -maxrregcount=N compiler command-line option (see nvcc) or the launch bounds kernel definition qualifier (see Execution Configuration of the CUDA C++ Programming Guide) to control the maximum number of registers to allocated per thread. No license, either expressed or implied, is granted under any NVIDIA patent right, copyright, or other NVIDIA intellectual property right under this document. For some fractional exponents, exponentiation can be accelerated significantly compared to the use of pow() by using square roots, cube roots, and their inverses. 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. On devices that are capable of concurrent copy and compute, it is possible to overlap kernel execution on the device with data transfers between the host and the device. (It should be mentioned that it is not possible to overlap a blocking transfer with an asynchronous transfer, because the blocking transfer occurs in the default stream, so it will not begin until all previous CUDA calls complete. A very important performance consideration in programming for CUDA-capable GPU architectures is the coalescing of global memory accesses. The following complete code (available on GitHub) illustrates various methods of using shared memory. We want to ensure that each change we make is correct and that it improves performance (and by how much). This is because some operations common to each element can be performed by the thread once, amortizing the cost over the number of shared memory elements processed by the thread. Figure 6 illustrates such a situation; in this case, threads within a warp access words in memory with a stride of 2. The NVIDIA nvcc compiler driver converts .cu files into C++ for the host system and CUDA assembly or binary instructions for the device. This chapter examines issues that can affect the correctness of returned data and points to appropriate solutions. 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. 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. The dimension and size of blocks per grid and the dimension and size of threads per block are both important factors. 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. The throughput of individual arithmetic operations is detailed in the CUDA C++ Programming Guide. 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. Thedriver will honor the specified preference except when a kernel requires more shared memory per thread block than available in the specified configuration.