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A lightweight infrastructure for graph analytics

This paper argues that existing DSLs can be implemented on top of a general-purpose infrastructure that supports very fine-grain tasks, implements autonomous, speculative execution of these tasks, and allows application-specific control of task scheduling policies.
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A quantitative study of irregular programs on GPUs

This paper defines two measures of irregularity called control-flow irregularity and memory-access irregularity, and investigates, using performance-counter measurements, how irregular GPU kernels differ from regular kernels with respect to these measures.
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I-structures: data structures for parallel computing

It is difficult to achieve elegance, efficiency, and parallelism simultaneously in functional programs that manipulate large data structures, and it is shown that even in the context of purely functional languages, I-structures are invaluable for implementing functional data abstractions.
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The tao of parallelism in algorithms

It is suggested that the operator formulation and tao-analysis of algorithms can be the foundation of a systematic approach to parallel programming.
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Lonestar: A suite of parallel irregular programs

The first five programs from the Lonestar benchmark suite are characterized, which target domains like data mining, survey propagation, and design automation, and it is shown that even such irregular applications often expose large amounts of parallelism in the form of amorphous data-parallelism.
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Optimistic parallelism requires abstractions

It is shown that Delaunay mesh generation and agglomerative clustering can be parallelized in a straight-forward way using the Galois approach, and results suggest that Galois is a practical approach to exploiting data parallelism in irregular programs.
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The program structure tree: computing control regions in linear time

A linear-time algorithm for finding SESE regions and for building the PST of arbitrary control flow graphs (including irreducible ones) is given and it is shown how to use the algorithm to find control regions in linear time.
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An Efficient CUDA Implementation of the Tree-Based Barnes Hut n-Body Algorithm

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Process decomposition through locality of reference

A system which, given a sequential program and its domain decomposition, performs process decomposition so as to enhance spatial locality of reference and an application - generating code from shared-memory programs for the (distributed memory) Intel iPSC/2.
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Data-centric multi-level blocking

This work presents a simple and novel framework for generating blocked codes for high-performance machines with a memory hierarchy based on reasoning directly about the flow of data through the memory hierarchy, which permits a more direct solution to the problem of enhancing data locality.
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