SC12 Home > SC12 Schedule > SC12 Presentation - A Novel Hybrid CPU-GPU Generalized Eigensolver for Electronic Structure Calculations Based on Fine Grained Memory Aware Tasks

SCHEDULE: NOV 10-16, 2012

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A Novel Hybrid CPU-GPU Generalized Eigensolver for Electronic Structure Calculations Based on Fine Grained Memory Aware Tasks

SESSION: Research Poster Reception

EVENT TYPE: Posters and Electronic Posters

TIME: 5:15PM - 7:00PM

SESSION CHAIR: Torsten Hoefler

AUTHOR(S):Raffaele Solcà, Azzam Haidar, Stanimire Tomov, Thomas C. Schulthess, Jack Dongarra

ROOM:East Entrance

ABSTRACT:
The adoption of hybrid GPU-CPU nodes in traditional supercomputing platforms such as the Cray-XK6 opens acceleration opportunities for electronic structure calculations in materials science and chemistry applications, where medium-sized generalized eigenvalue problems must be solved many times. These eigenvalue problems are too small to scale on distributed systems, but can benefit from the massive compute performance concentrated on a single node, hybrid GPU-CPU system. However, hybrid systems call for the development of new algorithms that efficiently exploit heterogeneity and massive parallelism of not just GPUs, but of multi/many-core CPUs as well. Addressing these demands, we developed a novel algorithm featuring innovative: Fine grained memory aware tasks; Hybrid execution/scheduling, and Increased computational intensity. The resulting eigensolvers are state-of-the-art in HPC, significantly outperforming existing libraries. We describe the algorithm and analyze its performance impact on applications of interest when different fractions of eigenvectors are needed by the host electronic structure code.

Chair/Author Details:

Torsten Hoefler (Chair) - ETH Zurich

Raffaele Solcà - ETH Zurich

Azzam Haidar - University of Tennessee, Knoxville

Stanimire Tomov - University of Tennessee, Knoxville

Thomas C. Schulthess - ETH Zurich

Jack Dongarra - University of Tennessee, Knoxville

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A Novel Hybrid CPU-GPU Generalized Eigensolver for Electronic Structure Calculations Based on Fine Grained Memory Aware Tasks

SESSION: Research Poster Reception

EVENT TYPE:

TIME: 5:15PM - 7:00PM

SESSION CHAIR: Torsten Hoefler

AUTHOR(S):Raffaele Solcà, Azzam Haidar, Stanimire Tomov, Thomas C. Schulthess, Jack Dongarra

ROOM:East Entrance

ABSTRACT:
The adoption of hybrid GPU-CPU nodes in traditional supercomputing platforms such as the Cray-XK6 opens acceleration opportunities for electronic structure calculations in materials science and chemistry applications, where medium-sized generalized eigenvalue problems must be solved many times. These eigenvalue problems are too small to scale on distributed systems, but can benefit from the massive compute performance concentrated on a single node, hybrid GPU-CPU system. However, hybrid systems call for the development of new algorithms that efficiently exploit heterogeneity and massive parallelism of not just GPUs, but of multi/many-core CPUs as well. Addressing these demands, we developed a novel algorithm featuring innovative: Fine grained memory aware tasks; Hybrid execution/scheduling, and Increased computational intensity. The resulting eigensolvers are state-of-the-art in HPC, significantly outperforming existing libraries. We describe the algorithm and analyze its performance impact on applications of interest when different fractions of eigenvectors are needed by the host electronic structure code.

Chair/Author Details:

Torsten Hoefler (Chair) - ETH Zurich

Raffaele Solcà - ETH Zurich

Azzam Haidar - University of Tennessee, Knoxville

Stanimire Tomov - University of Tennessee, Knoxville

Thomas C. Schulthess - ETH Zurich

Jack Dongarra - University of Tennessee, Knoxville

Add to iCal  Click here to download .ics calendar file

Add to Outlook  Click here to download .vcs calendar file

Add to Google Calendarss  Click here to add event to your Google Calendar