Workshop on Numerical Algorithms on Hybrid Architectures

Venue and location

The 10th International Conference on Parallel Processing and Applied Mathematics (PPAM 2013)

Polish-Japanese Institute of Information Technology, Warsaw, Poland

Dates:

September 8-11, 2013

Abstract The workshop is devoted to numerical algorithms with the particular attention to issues related to implementation of libraries for efficient numerical algorithms on modern hybrid architectures.

Organizers

• Jerzy Wasniewski, Danish Technical University Lyngby, Copenhagen, Denmark
• Przemyslaw Stpiczynski Maria Curie-Sklodowska University Lublin, Poland

Presention Details

• 9:30-9:55 Accelerating Dense Numerical Linear Algebra with Intel Xeon Phi (MIC) Coprocessors, Piotr Luszczek, University of Tennessee, Knoxville, USA

  Abstract. This paper presents the design and implementation of several fundamental dense linear algebra (DLA) algorithms for multicore with Intel Xeon Phi Coprocessors. In particular, we consider algorithms for solving linear systems. Further, we give an overview of the MAGMA MIC library, an open source, high performance library that incorporates the developments presented, and in general provides to heterogeneous architectures of multicore with coprocessors the DLA functionality of the popular LAPACK library. The LAPACK-compliance simplifies the use of the MAGMA MIC library in applications, while providing them with portably performant DLA. High performance is obtained through use of the high-performance BLAS, hardware-specific tuning, and a hybridization methodology where we split the algorithm into computational tasks of various granularities. Execution of those tasks is properly scheduled over the heterogeneous hardware components by minimizing data movements and mapping algorithmic requirements to the architectural strengths of the various heterogeneous hardware components. Our methodology and programming techniques are incorporated into the MAGMA MIC API, which abstracts the application developer from the specifics of the Xeon Phi architecture and is therefore applicable to algorithms beyond the scope of DLA.

• Accelerating Dense Numerical Linear Algebra with Intel Xeon Phi (MIC) Coprocessors