State-of-the-art deep neural networks (DNNs) have hundreds of millions of connections and
are both computationally and memory intensive, making them difficult to deploy on …

Convolutional Neural Networks (CNNs) have emerged as a fundamental technology for
machine learning. High performance and extreme energy efficiency are critical for …

Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning
(DL) is already present in many applications ranging from computer vision for medicine to …

Sparse matrix-vector multiplication (SpMV) is of singular importance in sparse linear
algebra. In contrast to the uniform regularity of dense linear algebra, sparse operations …

Everyone who has seen the movie Wall Street will have seen a commercial security policy in
action. The recent work of Clark and Wilson and the WIPCIS initiative (the Workshop on …

The traditional metric for the efficiency of a numerical algorithm has been the number of
arithmetic operations it performs. Technological trends have long been reducing the time to …

We are witnessing a dramatic change in computer architecture due to the multicore
paradigm shift, as every electronic device from cell phones to supercomputers confronts …

Sparse matrix-vector multiplication (SpMV) is a fundamental building block for numerous
applications. In this paper, we propose CSR5 (Compressed Sparse Row 5), a new storage …

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 …

We present a performance model-driven framework for automated performance tuning
(autotuning) of sparse matrix-vector multiply (SpMV) on systems accelerated by graphics …