Algorithmic Issues on Heterogeneous Computing Platforms

Static LU Decomposition on Heterogeneous Platforms

In this paper, the authors deal with algorithmic issues on heterogeneous platforms. They concentrate on dense linear algebra kernels, such as matrix multiplication or LU decomposition. Block-cyclic distribution techniques used in ScaLAPACK are no longer sufficient to balance the load among processors running at different speeds. The main result of this paper is to provide a static data distribu...

Algorithmi Issues on Heterogeneous Computing Platforms

Algorithmic Species: Classifying Program Code for Parallel Computing

Performance growth of single-core processors has come to a halt in the past decade, but was re-enabled by the introduction of parallelism in processors. Emerging processor architectures such as multi-cores and many-cores exploit an increasing amount of parallelism, requiring programmers and compilers to deal with issues such as threading, concurrency and synchronization. We advocate that progra...

Design of Application Software for Embedded Signal Processing

High Performance Computing (HPC) technology is being used to provide scalable and cost-eeective solutions to many Embedded Signal Processing (ESP) applications. Such applications are computationally intensive and require real-time performance. To meet such requirements, algorithmic techniques are needed to eeectively utilize available HPC platforms. In this paper, we address the design issues i...

Design of Application Software for Embeddable Signal Processing

High Performance Computing (HPC) technology is being used to provide scalable and cost-eeective solutions to many Embedded Signal Processing (ESP) applications. Such applications are computationally intensive and require real-time performance. To meet such requirements, algorithmic techniques are needed to eeectively utilize available HPC platforms. In this paper, we address the design issues i...