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. The warp wide reduction operations support arithmetic add, min, and max operations on 32-bit signed and unsigned integers and bitwise and, or and xor operations on 32-bit unsigned integers.įor more details on the new warp wide reduction operations refer to Warp Reduce Functions 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. ![]() Warp level support for Reduction Operations For more information on the Arrive/Wait Barriers refer to the Arrive/Wait Barrier section in the CUDA C++ Programming Guide. These barriers can also be used alongside the asynchronous copy. These barriers can be used to implement fine grained thread controls, producer-consumer computation pipeline and divergence code patterns in CUDA. The NVIDIA Ampere GPU architecture adds hardware acceleration for a split arrive/wait barrier in shared memory. Hardware Acceleration for Split Arrive/Wait Barrier For more information please refer to the section on Async Copy in the CUDA C++ Programming Guide. This new feature is exposed via the pipeline API in CUDA. These instructions also avoid using extra registers for memory copies and can also bypass the L1 cache. These copy instructions are asynchronous, with respect to computation and allow users to explicitly control overlap of compute with data movement from global memory into the SM. The NVIDIA Ampere GPU architecture adds hardware acceleration for copying data from global memory to shared memory. Asynchronous Data Copy from Global Memory to Shared Memory Overall, developers can expect similar occupancy as on Volta without changes to their application. For GPUs with compute capability 8.6 maximum shared memory per thread block is 99 KB. For GPUs with compute capability 8.6, shared memory capacity per SM is 100 KB.įor devices of compute capability 8.0 (i.e., A100 GPUs) the maximum shared memory per thread block is 163 KB. 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.įor devices of compute capability 8.0 (i.e., A100 GPUs) shared memory capacity per SM is 164 KB, a 71% increase compared to V100’s capacity of 96 KB. The maximum number of registers per thread is 255. The register file size is 64K 32-bit registers per SM. Other factors influencing warp occupancy are: The maximum number of concurrent warps per SM remains the same as in Volta (i.e., 64) for compute capability 8.0, while for compute capability 8.6 it is 48. The NVIDIA Ampere GPU architecture’s Streaming Multiprocessor (SM) provides the following improvements over Volta and Turing. NVIDIA Ampere GPU Architecture Tuning 1.4.1. Minimize redundant accesses to global memory whenever possible.Īvoid long sequences of diverged execution by threads within the same warp.īefore 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. ![]() Minimize data transfers between the host and the device.Īdjust kernel launch configuration to maximize device utilization.Įnsure global memory accesses are coalesced. The high-priority recommendations from those guides are as follows:įind ways to parallelize sequential code. Programmers must primarily focus on following those recommendations to achieve the best performance. The performance guidelines and best practices described in the CUDA C++ Programming Guide and the CUDA C++ Best Practices Guide apply to all CUDA-capable GPU architectures. 1įor further details on the programming features discussed in this guide, please refer to the CUDA C++ Programming Guide. This guide summarizes the ways that an application can be fine-tuned to gain additional speedups by leveraging the NVIDIA Ampere GPU architecture’s features. The NVIDIA Ampere GPU architecture retains and extends the same CUDA programming model provided by previous NVIDIA GPU architectures such as Turing and Volta, and applications that follow the best practices for those architectures should typically see speedups on the NVIDIA A100 GPU without any code changes. The NVIDIA Ampere GPU architecture is NVIDIA’s latest architecture for CUDA compute applications. NVIDIA Ampere GPU Architecture Tuning Guide 1.1. The programming guide for tuning CUDA Applications for GPUs based on the NVIDIA Ampere GPU Architecture. Tuning CUDA Applications for NVIDIA Ampere GPU Architecture
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