International audienceWe present a method for automatically selecting optimal implementations of sparse matrix-vector operations. Our software “AcCELS” (Accelerated Compress-storage Elements for Linear Solvers) involves a setup phase that probes machine characteristics, and a run-time phase where stored characteristics are combined with a measure of the actual sparse matrix to find the optimal kernel implementation. We present a performance model that is shown to be accurate over a large range of matrices
The widespread adoption of massively parallel processors over the past decade has fundamentally tran...
Abstract. We present new performance models and more compact data structures for cache blocking when...
This dissertation presents an architecture to accelerate sparse matrix linear algebra,which is among...
Abstract. This paper presents uniprocessor performance optimizations, automatic tuning techniques, a...
Abstract. Sparse matrix-vector multiplication is an important computational kernel that tends to per...
Sparse kernel performance depends on both the matrix and hardware platform. � Challenges in tuning s...
Application performance dominated by a few computational kernels Performance tuning today Vendor-tun...
The sparse matrix-vector product (SpMV) is a fundamental operation in many scientific applications f...
We consider the problem of building high-performance implementations of sparse matrix-vector multipl...
This report has been developed over the work done in the deliverable [Nava94] There it was shown tha...
AbstractSparse matrix vector multiplication (SpMV) is the dominant kernel in scientific simulations....
Abstract—This paper presents a performance modeling and optimization analysis tool to predict and op...
Many data mining algorithms rely on eigenvalue computations or iterative linear solvers in which the...
In this paper, we propose a lightweight optimization methodology for the ubiquitous sparse matrix-ve...
In previous work it was found that cache blocking of sparse matrix vector multiplication yielded sig...
The widespread adoption of massively parallel processors over the past decade has fundamentally tran...
Abstract. We present new performance models and more compact data structures for cache blocking when...
This dissertation presents an architecture to accelerate sparse matrix linear algebra,which is among...
Abstract. This paper presents uniprocessor performance optimizations, automatic tuning techniques, a...
Abstract. Sparse matrix-vector multiplication is an important computational kernel that tends to per...
Sparse kernel performance depends on both the matrix and hardware platform. � Challenges in tuning s...
Application performance dominated by a few computational kernels Performance tuning today Vendor-tun...
The sparse matrix-vector product (SpMV) is a fundamental operation in many scientific applications f...
We consider the problem of building high-performance implementations of sparse matrix-vector multipl...
This report has been developed over the work done in the deliverable [Nava94] There it was shown tha...
AbstractSparse matrix vector multiplication (SpMV) is the dominant kernel in scientific simulations....
Abstract—This paper presents a performance modeling and optimization analysis tool to predict and op...
Many data mining algorithms rely on eigenvalue computations or iterative linear solvers in which the...
In this paper, we propose a lightweight optimization methodology for the ubiquitous sparse matrix-ve...
In previous work it was found that cache blocking of sparse matrix vector multiplication yielded sig...
The widespread adoption of massively parallel processors over the past decade has fundamentally tran...
Abstract. We present new performance models and more compact data structures for cache blocking when...
This dissertation presents an architecture to accelerate sparse matrix linear algebra,which is among...