This article presents various data redistribution methods for block-partitioned linear algebra
algorithms operating on dense matrices that are distributed in a block-cyclic fashion …

Mixed-parallelism, the combination of data-and task-parallelism, is a powerful way of
increasing the scalability of entire classes of parallel applications on platforms comprising …

FPGAs can perform better than ASICs if the logic mapped onto them is optimized for each
problem instance. Unfortunately, this advantage is often canceled by the long time needed …

Run-time array redistribution is necessary to enhance the performance of parallel programs
on distributed memory supercomputers. In this paper, we present an efficient algorithm for …

In many scientific applications, array redistribution is usually required to enhance data
locality and reduce remote memory access in many parallel programs on distributed …

Array redistribution is required often in programs on distributed memory parallel computers.
It is essential to use efficient algorithms for redistribution; otherwise the performance of the …

Modern large-scale deep learning workloads highlight the need for parallel execution
across many devices in order to fit model data into hardware accelerator memories. In these …

This paper presents a new partitioning scheme for fractal image coding, based on triangles
and quadrilaterals. The aim is to have the advantage of the triangles over the square and …

Array redistribution is required often in programs on distributed memory parallel computers.
It is essential to use efficient algorithms for redistribution, otherwise the performance of the …

This article presents an extension of the library to handle parallel routines. is a dynamic
performance forecasting tool in a metacomputing environment. Here we propose to combine …