Abstract: The paper presents an architecture and an implementation of an FPGA-based sparse matrix-vector multiplier (SMVM) for use in the iterative solution of large, sparse systems of equations ...

Tutorials contains the code accompanying the HIP Tutorials that can be found in the HIP documentation. For a full overview over the examples see the section repository contents. Only a subset of the ...

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Dynamic Programming (DP) is a powerful technique for solving complex problems by breaking them into smaller, overlapping subproblems and avoiding redundant calculations. It’s a must-know skill for ...

Precision has long been the central bottleneck of analogue computing. Bit-slicing or analogue compensation can be used to perform matrix–vector multiplication with precision, but solving matrix ...

Cyclops is a parallel (distributed-memory) numerical library for multidimensional arrays (tensors) in C++ and Python. Quick documentation links: C++ and Python. Broadly, Cyclops provides tensor ...

PDC Center for High Performance Computing, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden Division of Theoretical Chemistry and Biology, School of Engineering Sciences in Chemistry, ...

Abstract: This paper presents a performance modeling and optimization analysis tool to predict and optimize the performance of sparse matrix-vector multiplication (SpMV) on GPUs. We make the following ...

Over the past decade, Graphics Processing Units (GPUs) have revolutionized high-performance computing, playing pivotal roles in advancing fields like IoT, autonomous ...

Matrix computation, as a fundamental building block of information processing in science and technology, contributes most of the computational overheads in modern signal processing and artificial ...

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