'Gpu performance' diaporamas de présentation

High Performance Computing: Technologies and Opportunities

High Performance Computing: Technologies and Opportunities. Dr. Charles J Antonelli LSAIT ARS May, 2013. ES13 Mechanics. Welcome! Please sign in If registered, check the box next to your name If walk-in, please write your name, email, standing, unit, and department

822 views • 67 slides

The Intersection of Game Engines & GPUs: Current & Future

2.5. The Intersection of Game Engines & GPUs: Current & Future. Johan Andersson Rendering Architect. Agenda. Goal Share and discuss current & future graphics use cases in our games and implications for graphics hardware Areas Engine overview Shaders Parallelization Texturing

454 views • 45 slides

S03: High Performance Computing with CUDA Heterogeneous GPU Computing for Molecular Modeling

S03: High Performance Computing with CUDA Heterogeneous GPU Computing for Molecular Modeling. John E. Stone Theoretical and Computational Biophysics Group Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign http://www.ks.uiuc.edu/Research/gpu/

478 views • 29 slides

GPU Performance Prediction

Javier Delgado Gabriel Gazolla Constantinos Menelaou Lixi Wang Mark Joselli. GPU Performance Prediction. Outline. Motivation Role in Energy Efficiency Performance Modeling GPU programming for Weather Modeling GPU Programming for BLAST Model Testing Conclusion. Benefits.

399 views • 25 slides

Debunking the 100X GPU vs CPU Myth: An Evaluation of Throughput Computing on CPU and GPU

261 views • 24 slides

Boosting Mobile GPU Performance with a Decoupled Access/Execute Fragment Processor

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Findings from Top 500

Findings from Top 500. Architecture (Clayton and Josh Kidd). Number of Cores (Ethan and Akash ). Performance vs. Processor Count ( Ramanuja ). GPU Performance (Marcy). Scalar vs. Vector Architecture (Nick). Power Considerations (Josh Kuhn). Sector Trends (Eben).

233 views • 13 slides

GPGPU Introduction

GPGPU Introduction. Alan Gray EPCC The University of Edinburgh. Introduction. Central Processing Unit (CPU) of a computer system must be able to perform a wide variety of tasks efficiently.

184 views • 7 slides

More Charm++/TAU examples

More Charm++/TAU examples. Applications: NAMD Parallel Framework for Unstructured Meshing ( ParFUM ) Features: Profile snapshots: Captures the runtime of the application by segregating it into user specified intervals CUDA Profiling Tracks time spent in CUDA kernel routines

195 views • 5 slides