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Roofline: an insightful visual performance model for multicore architectures
Roofline: An insightful Visual Performance model for multicore Architectures Page 1 APriL 2009
| voL. 52 | no. 4 | communicAtionS of the Acm 65 conVentional WiSdom in computer architecture …
| voL. 52 | no. 4 | communicAtionS of the Acm 65 conVentional WiSdom in computer architecture …
[HTML][HTML] The landscape of parallel computing research: A view from berkeley
The recent switch to parallel microprocessors is a milestone in the history of computing.
Industry has laid out a roadmap for multicore designs that preserves the programming …
Industry has laid out a roadmap for multicore designs that preserves the programming …
Sparsep: Towards efficient sparse matrix vector multiplication on real processing-in-memory architectures
Several manufacturers have already started to commercialize near-bank Processing-In-
Memory (PIM) architectures, after decades of research efforts. Near-bank PIM architectures …
Memory (PIM) architectures, after decades of research efforts. Near-bank PIM architectures …
OSKI: A library of automatically tuned sparse matrix kernels
Abstract The Optimized Sparse Kernel Interface (OSKI) is a collection of low-level primitives
that provide automatically tuned computational kernels on sparse matrices, for use by solver …
that provide automatically tuned computational kernels on sparse matrices, for use by solver …
TileSpGEMM: A tiled algorithm for parallel sparse general matrix-matrix multiplication on GPUs
Sparse general matrix-matrix multiplication (SpGEMM) is one of the most fundamental
building blocks in sparse linear solvers, graph processing frameworks and machine learning …
building blocks in sparse linear solvers, graph processing frameworks and machine learning …
A systematic literature survey of sparse matrix-vector multiplication
Sparse matrix-vector multiplication (SpMV) is a crucial computing kernel with widespread
applications in iterative algorithms. Over the past decades, research on SpMV optimization …
applications in iterative algorithms. Over the past decades, research on SpMV optimization …
[ΒΙΒΛΙΟ][B] Automatic performance tuning of sparse matrix kernels
RW Vuduc - 2003 - search.proquest.com
This dissertation presents an automated system to generate highly efficient, platform-
adapted implementations of sparse matrix kernels. We show that conventional …
adapted implementations of sparse matrix kernels. We show that conventional …
Tilespmv: A tiled algorithm for sparse matrix-vector multiplication on gpus
With the extensive use of GPUs in modern supercomputers, accelerating sparse matrix-
vector multiplication (SpMV) on GPUs received much attention in the last couple of decades …
vector multiplication (SpMV) on GPUs received much attention in the last couple of decades …
Self-adapting linear algebra algorithms and software
One of the main obstacles to the efficient solution of scientific problems is the problem of
tuning software, both to the available architecture and to the user problem at hand. We …
tuning software, both to the available architecture and to the user problem at hand. We …
Fast sparse matrix-vector multiplication by exploiting variable block structure
RW Vuduc, HJ Moon - … , HPCC 2005, Sorrento, Italy, September 21-23 …, 2005 - Springer
We improve the performance of sparse matrix-vector multiplication (SpMV) on modern cache-
based superscalar machines when the matrix structure consists of multiple, irregularly …
based superscalar machines when the matrix structure consists of multiple, irregularly …