Titlebook: Languages and Compilers for Parallel Computing; 23rd International W Keith Cooper,John Mellor-Crummey,Vivek Sarkar Conference proceedings 2

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McFLAT: A Profile-Based Framework for MATLAB Loop Analysis and Transformations,nges are worth specializing using a variety of loop transformations..Our . framework has been implemented as part of the Mc. extensible compiler toolkit. Currently, ., is used to automatically transform ordinary . code into specialized . code with transformations applied to it. This specialized code

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Entropion

A Parallel Numerical Solver Using Hierarchically Tiled Arrays,implement two algorithms from the SPIKE family using the HTA library. We show that our implementations of SPIKE exploit the abstractions provided by the HTA to produce a compact, clean code that can run on both shared-memory and distributed-memory models without modification. We discuss how we map t

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Locality Optimization of Stencil Applications Using Data Dependency Graphs,one of the first Cyclops-64 many-core chips produced, confirm the effectiveness of our approach to reduce the total number of memory operations of stencil applications as well as the running time of the application.

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How Many Threads to Spawn during Program Multithreading?,ogram dependence standpoint, use of larger number of threads than advocated by the proposed approach does not yield higher degree of TLP. We present a couple of case studies and results using kernels, extracted from open source codes, to demonstrate the efficacy of our techniques on a real machine.

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Parallelizing Compiler Framework and API for Power Reduction and Software Productivity of Real-Timeous multicore chip named RP-X integrating 8 general purpose processor cores and 3 types of accelerator cores which was developed by Renesas Electronics, Hitachi, Tokyo Institute of Technology and Waseda University. The framework attains speedups up to 32x for an optical flow program with eight gener

DAFT

CnC-CUDA: Declarative Programming for GPUs,nts relative to general-purpose CPUs. Unfortunately, hybrid programming models that support multithreaded execution on CPUs in parallel with CUDA execution on GPUs prove to be too complex for use by mainstream programmers and domain experts, especially when targeting platforms with multiple CPU core