Richard vuduc thesis

The thesis proposes two classes of optimizations of this model.

Richard Wilson Vuduc: Curriculum Vitæ

These dynamically formed pockets of structured parallelism can utilize the recently introduced device-side nested kernel launch capabilities on GPUs.

In particular, we present a distributed tree structure to index data in arbitrary number of dimensions, and a novel algorithm that eliminate the need for collective coordinate exchanges during tree construction.

Finally, to show that our findings can be applied in real-world scenarios, we apply these techniques to the problem of verifying that a multiprocessor complies with its memory consistency model. By presenting the implementation, measurements and key insights, this thesis takes a step in addressing the challenges and issues in emerging irregular applications.

The objective of this thesis is the development, implementation and optimization of a GPU execution model extension that efficiently supports time-varying, nested, fine-grained dynamic parallelism occurring in the irregular data intensive applications.

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The award is sponsored by Springerthe international publisher specializing in science, technology and medicine. Essentially, all areas covered in the call for research papers also accepted for the PhD Forum presentations.

We further generalize this approach and provide an abstraction that can be applied to a whole class of graph algorithms that require many simultaneous breadth-first searches. The following information needs to be provided in the online submission form: Tuesday, September 22nd, Time: Molecular simulation is an indispensable tool in many different disciplines such as physics, biology, chemical engineering, materials science, drug design, and others.

Further, two important characteristics of real world graphs like those in social networks are that they are big and are constantly evolving over time. And because of the high rate at which these large-scale graphs evolve, it is undesirable and computationally infeasible to repeatedly run static graph analytics on a sequence of versions, or Richard vuduc thesis, of the evolving graph.

This poses challenge due to limitations in GPU-resident memory for storing these large graphs. Evaluations of DTBL shows an average of 1. In this dissertation, I explore parallel algorithms for general N-Body problems in high dimensions, and their applications in machine learning and image analysis on distributed infrastructures.

On the other hand, molecular simulation methods usually have very steep computational costs, which limits current molecular simulation studies to relatively small systems. PhD Forum presenters need to be registered ISC participants, and only posters of registered presenters will be admitted.

In the first part of this work, we proposed and developed a set of basic tools built on top of Message Passing Interface and OpenMP for massively parallel nearest neighbors search.

Experimental results show our framework is robust to noise, variations of shapes and artifacts. It also characterizes various graph algorithms and how related graph properties affect the complexity of incremental graph processing in making runtime decisions to choose between an incremental vs static run over a particular update batch to achieve the best performance.

Then a parallel matrix free optimization algorithm is given to solve the MAP estimation. The parallel techniques and methods developed in this work can be also applied to other molecular simulation applications. Santosh Pande, CS Abstract: However, many existing algorithms and codes for molecular simulation are from more than a decade ago, which were designed for sequential computers or early parallel architectures.

During the poster session, the presenters are required to be available at their posters for discussions.Memory Hierarchy Optimizations and Performance Bounds for Sparse AT Ax Richard Vuduc Attila Gyulassy James W. Demmel Katherine A. Yelick Report No.

UCB/CSD February Computer Science Division (EECS) University of California Berkeley, California Abstract This report presents uniprocessor automatic tuning. Automatic Performance Tuning or "Auto-tuning", is an empirical, feedback-driven performance optimization technique designed to maximize performance across a wide variety of architectures without sacrificing portability or productivity.

CSE Graduate Student Handbook 3 PROGRAM DESCRIPTION AND OBJECTIVES Computational!science!and!engineering!(CSE)!is!the!systematic!study!of!computerNbasedmodels!of! • Motivation for Automatic Performance Tuning • Results for sparse matrix kernels Richard Vuduc, J.

Demmel, and Jeff A. Bilmes. See p. of Vuduc’s thesis for matrices Accuracy of the Tuning Heuristics (2/4) 9. Automatic Performance Tuning and Sparse-Matrix-Vector-Multiplication (SpMV) James Demmel Richard Vuduc, J. Demmel, and Jeff A. Bilmes. See p. of Vuduc’s thesis for matrices Accuracy of the Tuning Heuristics (2/4) 9.

I received my PhD degree in Electrical and Computer Engineering from Georgia Institute of Technology inwhere I worked on performance and energy modeling for high performance computing mint-body.com PhD thesis advisor was.

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