common scenarios. In the context of shared-memory multicore installations, we show how high performance and scalability go hand in hand, when the well-known linear algebra algorithms are recast in terms of Direct Acyclic Graphs (DAGs), which are then transparently scheduled at runtime inside the Parallel

We present performance results for dense linear algebra using recent NVIDIA GPUs. Our matrix-matrix multiply routine (GEMM) runs up to 60 % faster than the vendor’s implementation and approaches the peak of hardware capabilities. Our LU, QR and Cholesky factorizations achieve up to 80

on any heterogeneous network and is implemented as a fault-tolerant client-server application. Keywords Distributed System, Heterogeneity, Load Balancing, Client-Server, Fault Tolerance, Linear Algebra, Virtual Library. University of Tennessee - Technical report No cs-95-313 Department of Computer

This paper describes ScaLAPACK, a distributed memory version of the LAPACK software package for dense and banded matrix computations. Key design features are the use of distributed versions of the Level LAS as building blocks, and an ob ect-based interface to the library routines. The square block

Among the various factors that drive the momentous changes occurring in the design of microprocessors and high end systems [1], three stand out as especially notable: 1. the number of transistors per chip will continue the current trend, i.e. double roughly every 18 months, while the speed of processor clocks will cease to in-

In this paper, we study the implementation of dense linear algebra kernels, such as matrix multiplication or linear system solvers, on heterogeneous networks of workstations. The uniform block-cyclic data distribution scheme commonly used for homogeneous collections of processors limits

We present an efficient and scalable programming model for the development of linear algebra in heterogeneous multi-coprocessor environments. The model incorporates some of the current best design and implementation practices for the heterogeneous acceleration of dense linear algebra (DLA

components and building blocks of ScaLAPACK. This paper outlines the difficulties inherent in producing correct codes for networks of heterogeneous processors. We define a theoretical model of parallel computers dedicated to linear algebra applications: the Distributed Linear Algebra Machine (DLAM

Abstract. In the field of HPC, the current hardware trend is to design multiprocessor architectures that feature heterogeneous technologies such as specialized coprocessors (e.g., Cell/BE SPUs) or data-parallel accelerators (e.g., GPGPUs). Approaching the theoretical performance

This paper discusses the scalability of Cholesky, LU, and QR factorization routines on MIMD distributed memory concurrent computers. These routines form part of the ScaLAPACK mathematical software library that extends the widely-used LAPACK library to run efficiently on scalable concurrent