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In computing, CUDA (Compute Unified Device Architecture) is a proprietary [2] parallel computing platform and application programming interface (API) that allows software to use certain types of graphics processing units (GPUs) for accelerated general-purpose processing, an approach called general-purpose computing on GPUs.
CUDA operates on a heterogeneous programming model which is used to run host device application programs. It has an execution model that is similar to OpenCL. In this model, we start executing an application on the host device which is usually a CPU core. The device is a throughput oriented device, i.e., a GPU core which performs parallel ...
This is especially effective when the programmer wants to process many vertices or fragments in the same way. In this sense, GPUs are stream processors – processors that can operate in parallel by running one kernel on many records in a stream at once. A stream is simply a set of records that require similar computation. Streams provide data ...
Memory – The amount of graphics memory available to the processor. SM Count – Number of streaming multiprocessors. [1] Core clock – The factory core clock frequency; while some manufacturers adjust clocks lower and higher, this number will always be the reference clocks used by Nvidia.
Performance is also affected by the number of streaming multiprocessors (SM) for NVidia GPUs, or compute units (CU) for AMD GPUs, or Xe cores for Intel discrete GPUs, which describe the number of on-silicon processor core units within the GPU chip that perform the core calculations, typically working in parallel with other SM/CUs on the GPU.
A "Streaming Multiprocessor" is analogous to AMD's Compute Unit. An SM encompasses 128 single-precision ALUs ("CUDA cores") on GP104 chips and 64 single-precision ALUs on GP100 chips. While all CU versions consist of 64 shader processors (i.e. 4 SIMD Vector Units, each 16 lanes wide), Nvidia experimented with very different numbers of CUDA cores:
The Nvidia Hopper H100 GPU is implemented using the TSMC N4 process with 80 billion transistors. It consists of up to 144 streaming multiprocessors. [1] Due to the increased memory bandwidth provided by the SXM5 socket, the Nvidia Hopper H100 offers better performance when used in an SXM5 configuration than in the typical PCIe socket.
Streaming multiprocessors 144 80 60 36 24 CUDA cores: 18432 10240 7680 4608 3072 Texture mapping units: 576 320 240 144 96 Render output units: 192 112 80 48 32 Tensor cores: 576 320 240 144 96 RT cores: 144 80 60 36 24 L1 cache: 18 MB 10 MB 7.5 MB 4.5 MB 3 MB 128 KB per SM L2 cache 96 MB 64 MB 48 MB 32 MB