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Recent research studies have shown that modern GPU performance is often limited by the memory system performance. Optimizing memory hierarchy performance requires GPU designers to draw design insights based on the cache & memory behavior of end-user applications. Unfortunately, it is often difficult to get access to end-user workloads due to the confidential or proprietary nature of the software/data...
Programming models like CUDA, OpenMP, OpenACC and OpenCL are designed to offload compute-intensive workloads to accelerators efficiently. However, the naive offload model, which synchronously copies and executes in sequence, requires extensive hand-tuning of techniques, such as pipelining to overlap computation and communication. Therefore, we propose an easy-to-use, directive-based pipelining extension...
As various graphics processing unit architectures are deployed across broad computing spectrum from a hand-held or embedded device to a high-performance computing server, OpenCL becomes the de facto standard programming environment for general-purpose computing on graphics processing units. Unlike its CPU counterpart, OpenCL has several distinct features such as its disciplined memory model, which...
Unified Memory is an emerging technology which is supported by CUDA 6.X. Before CUDA 6.X, the existing CUDA programming model relies on programmers to explicitly manage data between CPU and GPU and hence increases programming complexity. CUDA 6.X provides a new technology which is called as Unified Memory to provide a new programming model that defines CPU and GPU memory space as a single coherent...
In this paper, we report on the development of an efficient GPU implementation of the Strassen-Winograd matrix multiplication algorithm for matrices of arbitrary sizes. We utilize multi-kernel streaming to exploit concurrency across sub-matrix operations in addition to intra-operation parallelism. We evaluate the performance of the implementation in comparison with CUBLAS-5.0 on Fermi and Kepler GPUs...
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