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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 ...
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.
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 ...
For instance, to handle an IF-ELSE block where various threads of a processor execute different paths, all threads must actually process both paths (as all threads of a processor always execute in lock-step), but masking is used to disable and enable the various threads as appropriate. Masking is avoided when control flow is coherent for the ...
The first product was released in late 2006, rebranded as AMD Stream Processor after the merger with AMD. [ 4 ] The brand became AMD FireStream with the second generation of stream processors in 2007, based on the RV650 chip with new unified shaders and double precision support. [ 5 ]
Most (90%) of a stream processor's work is done on-chip, requiring only 1% of the global data to be stored to memory. This is where knowing the kernel temporaries and dependencies pays. Internally, a stream processor features some clever communication and management circuits but what's interesting is the Stream Register File (SRF). This is ...
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:
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.