IEEE Computer Society - Multicore Video Series

IEEE Computer Society - Multicore Video Series
Tue, 1 December, 201503:13 PM, EDT (19:13, UTC)

This video lecture series features some of the most advanced parallelization, vectorization, and power-reduction technologies used in industry applications for multicore processors with accelerators. The talks explore applications in supercomputing, cloud computing, cluster computing, big data, automobiles from ECU to self-driving cars, multimedia, smartphones, medical image processing, base band communication, and much more. Discover how these experts utilize innovative methods and techniques to improve performance and reduce applications’ power consumption on multicore processors, as well as how these innovations help significantly reduce costs and time while improving reliability in parallel programs for multicores.

The material presented in this series is very useful in reinforcing the concepts presented in lectures on compilers, architectures, parallel programming, and embedded systems. Most important of all, this lecture series offers educators and students a window into the minds of some of the most accomplished experts in the field.

This series is presented by the IEEE Computer Society. To learn more about Computer Society membership, please visit https://www.computer.org/web/membership

Round Table

Round Table01:03:21
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Round Table

All participant round table

Vectorization/Parallelization in the IBM Compiler

Vectorization/Parallelization in the IBM Compiler 00:44:36
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Vectorization/Parallelization in the IBM Compiler

Yaoqing Gao is a senior technical staff member at IBM Canada Lab. His major interests are compilation technology, optimization and performance-tuning tools, parallel programming models and languages, and computer architecture. He has been doing research and development for IBM XL C/C++ and Fortran compiler products on IBM POWER, System z, CELL processors, and Blue Gene. He is an IBM Master inventor and has authored more than 30 issued and pending patents. Before joining IBM, Dr. Gao conducted research on parallel and distributed processing and programming languages at Tsinghua University, the National University of Singapore, the University of Tokyo, and the University of Alberta.

Autoparallelization for GPUs

Autoparallelization for GPUs01:17:20
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Autoparallelization for GPUs

Prof. Wen-Mei Hwu (University of Illinois at Urbana-Champaign) holds the Sanders-AMD Endowed Chair in the Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign. His research interests are in the area of architecture, implementation, compilation, and algorithms for parallel computing. He is the chief scientist of Parallel Computing Institute and director of the IMPACT research group (www.impact.crhc.illinois.edu). He is a co-founder and CTO of MulticoreWare. Hwu serves as one of the principal investigators of the NSF Blue Waters Petascale computer project. Dr. Hwu received his Ph.D. degree in Computer Science from the University of California, Berkeley.

Dependences and Dependence Analysis

Dependences and Dependence Analysis01:03:33
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Dependences and Dependence Analysis

Prof. Utpal Banerjee (University of California, Irvine) has published four books on loop transformations and dependence analysis, with a fifth one on instruction-level parallelism on the way. He has run international computer conferences and is a co-editor of the International Journal of Parallel Programming. He has a PhD in pure mathematics from Carnegie-Mellon University, a PhD in computer science from the University of Illinois, Urbana-Champaign, as well as an MSc in applied mathematics and a post MSc diploma in nuclear physics from Calcutta University.

Dynamic Parallelization

Dynamic Parallelization00:58:50
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Dynamic Parallelization

Prof. Rudolf Eigenman (School of Electrical and Computer Engineering, Purdue University) examines how to optimize compilers, programming methodologies and tools, performance evaluation for high-performance computers, and cyber-infrastructures. He currently serves as Program Director at the US National Science Foundation.

Vectorization/Parallelization in the Intel Compiler

Vectorization/Parallelization in the Intel Compiler00:50:16
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Vectorization/Parallelization in the Intel Compiler

Peng Tu is a Principle Engineer and manages the Technology Pathfinding engineering team in the Developer Product Division of Intel Corporation. Previously, he had also managed Intel Compiler's IA32/Intel64 global optimizer and code generation team. His teams developed Intel's C++ Extension of Array Notation and the Intel SPMD Compiler (ISCP). Peng has a PhD in Computer Science from University of Illinois at Urbana-Champaign in 1995. Prior to Intel, he worked on various compiler products at SGI and Tensilica Inc.

The Polyhedral Model

The Polyhedral Model01:22:00
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The Polyhedral Model

Paul Feautrier is now an emeritus professor at the Ecole Normale Sup�rieure de Lyon. He has been one of the prime movers behind the polyhedral model, an abstract representation of regular programs. Initially devised for automatic parallelization, this model is now used for program analysis and verification, code generation and many other topics.

Vectorization

Vectorization01:00:11
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Vectorization

Prof. P. Sadayappan (The Ohio State University) focuses on domain-specific compiler optimization and high-performance scientific computing. Some recent projects include the PolyOpt polyhedral optimizer in the ROSE compiler, and the Tensor Contraction Engine, a domain-specific compiler for automated synthesis of high-performance codes for tensor expressions arising in coupled cluster and other ab initio methods in quantum chemistry. Sadayappan is a Fellow of the IEEE.

Multigrain Parallelization and Power Reduction

Multigrain Parallelization and Power Reduction00:56:23
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Multigrain Parallelization and Power Reduction

Hironori Kasahara is a professor in the Department of Computer Science and Engineering and a director of the Advanced Multicore Processor Research Institute at Waseda University in Tokyo. He has been researching on OSCAR Automatic Parallelizing and Power reducing Compiler and OSCAR Multicore architecture for more than 30 years and led four Japanese national projects on parallelizing compilers, multicores and green computing. Kasahara has presented his work via more than 200 papers, 125 invited talks, 27 patents, and 490 newspapers and web articles.

Automatic Parallelization

Automatic Parallelization01:17:21
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Automatic Parallelization

Prof. David Padua (University of Illinois at Urbana-Champaign) has devoted much of his career to the study of languages, tools, and compilers for parallel computing. His PhD dissertation was one of the first studies on compiler techniques for multiprocessors. He has continued this work contributing with techniques for program analysis and program transformation as well as methodologies to evaluate the effectiveness of compilers.

Vector Computation

Vector Computation01:13:01
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Vector Computation

David Kuck is an Intel Fellow working on hardware/software codesign in Intel�s Software and Solutions Group. He was a professor of CS/ECE at the University of Illinois (UIUC) and founder of the Center for Supercomputing Research and Development. He was a founder and Chairman of KAI from 1979 until 2000 when it was acquired by Intel. He is a Fellow of the IEEE, ACM, and AAAS, and has received the IEEE Piore Award, the IEEE Computer Society�s Computer Pioneer Award, the ACM-IEEE Eckert-Mauchly and Kennedy Awards, and is a member of the US National Academy of Engineering.

Instruction-Level Parallelization

Instruction-Level Parallelization01:12:41
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Instruction-Level Parallelization

Prof. Alex Nicolau (University of California, Irvine) is chair of the computer science department at UCI, editor in chief of the International Journal of Parallel Programming, and author of more than 300 conference and journal articles and multiple books.

IEEE Computer Society - Multicore Video Series (All Videos) [12 Videos]

IEEE Computer Society - Multicore Video Series (All Videos)This video lecture series features some of the most advanced parallelization, vectorization, and power-reduction technologies used in industry applications for multicore processors with accelerators. The talks explore applications in supercomputing, cloud computing, cluster computing, big data, automobiles from ECU to self-driving cars, multimedia, smartphones, medical image processing, base band communication, and much more. Discover how these experts utilize innovative methods and techniques to improve performance and reduce applications? power consumption on multicore processors, as well as how these innovations help significantly reduce costs and time while improving reliability in parallel programs for multicores. The material presented in this series is very useful in reinforcing the concepts presented in lectures on compilers, architectures, parallel programming, and embedded systems. Most important of all, this lecture series offers educators and students a window into the minds of some of the most accomplished experts in the field.Included Videos