Introduction to algorithms
An introduction to parallel algorithms
An introduction to parallel algorithms
LogP: towards a realistic model of parallel computation
PPOPP '93 Proceedings of the fourth ACM SIGPLAN symposium on Principles and practice of parallel programming
LogP: a practical model of parallel computation
Communications of the ACM
Automatic selection of high-order transformations in the IBM XL FORTRAN compilers
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Computer architecture (2nd ed.): a quantitative approach
Computer architecture (2nd ed.): a quantitative approach
Space-time scheduling of instruction-level parallelism on a raw machine
Proceedings of the eighth international conference on Architectural support for programming languages and operating systems
Parallel Computer Architecture: A Hardware/Software Approach
Parallel Computer Architecture: A Hardware/Software Approach
Synthesis of Parallel Algorithms
Synthesis of Parallel Algorithms
Parallel Scientific Computing in C++ and MPI
Parallel Scientific Computing in C++ and MPI
Algorithm Design
Computer
Algorithms
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Parallel Programming in C with MPI and OpenMP
X10: concurrent programming for modern architectures
Proceedings of the 12th ACM SIGPLAN symposium on Principles and practice of parallel programming
PRAM-on-chip: first commitment to silicon
Proceedings of the nineteenth annual ACM symposium on Parallel algorithms and architectures
Transactional Memory: An Overview
IEEE Micro
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
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Proceedings of the 5th conference on Computing frontiers
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The International Parallel and Distributed Processing Symposium (IPDPS) 2008 panel with the title ''How to avoid making the same Mistakes all over again: What the parallel-processing Community has (failed) to offer the multi/many-core Generation'' sought to provoke discussion on current and recent computer science education in relation to the emergence of fundamentally parallel multi/many-core systems. Is today's/tomorrow's/yesterday's computer science graduate equipped to deal with the challenges of parallel software development for such systems? Are mistakes from the past being unnecessarily repeated? What are the fundamental contributions of the parallel processing research community to the current state of affairs that are possibly being ignored? What are the new challenges that have not been addressed in past parallel processing research? How should computer-science education in parallel processing look like? Should it be taught at all? To the extent that there was consensus among the panelists, they agreed on the premise for the panel, namely that there is a mismatch in computer-science education concerning parallelism, and that there may be reasons to be concerned. They agreed on stressing the importance of (a) applications as a driving factor in research and education, (b) parallel algorithms, and of (c) focusing on the ease of parallel programming and not exclusively on parallel performance, and cited for instance heterogeneous parallelism and power awareness as new issues for the multi-core generation. The panelists were Hideharu Amano (Keio University), John Gustafson (Clearspeed Technologies), Keshav Pingali (University of Austin, Texas), Vivek Sarkar (Rice University), Uzi Vishkin (University of Maryland), and Katherine Yelick (University of California at Berkeley). The panel was organized and moderated by the author.