Piotr Luszczek

Numerical linear algebra on emerging architectures: The PLASMA and MAGMA projects

The emergence and continuing use of multi-core architectures and graphics processing units require changes in the existing software and sometimes even a redesign of the established algorithms in order to take advantage of now prevailing parallelism. Parallel Linear Algebra for Scalable Multi-core Architectures (PLASMA) and Matrix Algebra on GPU and Multics Architectures (MAGMA) are two projects that aims to achieve high performance and portability across a wide range of multi-core architectures and hybrid systems respectively. We present in this document a comparative study of PLASMAs performance against established linear algebra packages and some preliminary results of MAGMA on hybrid multi-core and GPU systems.

Accelerating scientific computations with mixed precision algorithms

On modern architectures, the performance of 32-bit operations is often at least twice as fast as the performance of 64-bit operations. By using a combination of 32-bit and 64-bit floating point arithmetic, the performance of many dense and sparse linear algebra algorithms can be significantly enhanced while maintaining the 64-bit accuracy of the resulting solution. The approach presented here can apply not only to conventional processors but also to other technologies such as Field Programmable Gate Arrays (FPGA), Graphical Processing Units (GPU), and the STI Cell BE processor. Results on modern processor architectures and the STI Cell BE are presented.

Using Mixed Precision for Sparse Matrix Computations to Enhance the Performance while Achieving 64-bit Accuracy

By using a combination of 32-bit and 64-bit floating point arithmetic, the performance of many sparse linear algebra algorithms can be significantly enhanced while maintaining the 64-bit accuracy of the resulting solution. These ideas can be applied to sparse multifrontal and supernodal direct techniques and sparse iterative techniques such as Krylov subspace methods. The approach presented here can apply not only to conventional processors but also to exotic technologies such as Field Programmable Gate Arrays (FPGA), Graphical Processing Units (GPU), and the Cell BE processor.

The PlayStation 3 for High-Performance Scientific Computing

Is real-world gaming technology the next big thing in the more academically based high-performance computing arena? The authors put PlayStation 3 to the test.

DARPA's HPCS Program- History, Models, Tools, Languages

Abstract The historical context with regard to the origin of the DARPA High Productivity Computing Systems (HPCS) program is important for understanding why federal government agencies launched this new, long-term high-performance computing program and renewed their commitment to leadership computing in support of national security, large science and space requirements at the start of the 21st century. In this chapter, we provide an overview of the context for this work as well as various procedures being undertaken for evaluating the effectiveness of this activity including such topics as modelling the proposed performance of the new machines, evaluating the proposed architectures, understanding the languages used to program these machines as well as understanding programmer productivity issues in order to better prepare for the introduction of these machines in the 2011–2015 timeframe.

BONSAI: an OpeN Software Autotuning Infrastructure

The goal of the BONSAI project is to develop a software infrastructure for using parallel hybrid systems at any scale to carry out large, concurrent autotuning sweeps in order to dramatically accelerate the optimization process of computational kernels for GPU accelerators and many-core coprocessors.

Changes in Dense Linear Algebra Kernels: Decades-Long Perspective

Over the years, computational physics and chemistry served as an ongoing 5 source of problems that demanded the ever increasing performance from 6 hardware as well as the software that ran on top of it. Most of these 7 problems could be translated into solutions for systems of linear equa8 tions: the very topic of numerical linear algebra. Seemingly then, a set of 9 efficient linear solvers could be solving important scientific problems for 10 years to come. We argue that dramatic changes in hardware designs pre11 cipitated by the shifting nature of the marketplace of computer hardware 12 had a continuous effect on the software for numerical linear algebra. The 13 extraction of high percentages of peak performance continues to require 14 adaptation of software. If the past history of this adaptive nature of linear 15 algebra software is any guide then the future theme will feature changes 16 as well — changes aimed at harnessing the incredible advances of the 17 evolving hardware infrastructure. 18