PPOPP 2010 - PDF Preprint Papers

PPOPP 2010 Principles and Practice of Parallel Programming

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PPOPP
2010
ACM

218views Distributed and Parallel Com...» more PPOPP 2010»

Data transformations enabling loop vectorization on multithreaded data parallel architectures

1 years 11 months ago

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Loop vectorization, a key feature exploited to obtain high performance on Single Instruction Multiple Data (SIMD) vector architectures, is significantly hindered by irregular memo...

Byunghyun Jang, Perhaad Mistry, Dana Schaa, Rodrig...

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Id	Views	Title	Status
1	218	Data transformations enabling loop vectorization on multithreaded data parallel architectures	claim
2	203	Thread to strand binding of parallel network applications in massive multi-threaded systems	claim
3	132	A distributed placement service for graph-structured and tree-structured data	claim
4	122	A practical concurrent binary search tree	claim
5	121	Does cache sharing on modern CMP matter to the performance of contemporary multithreaded programs?	claim
6	111	Load balancing on speed	claim
7	109	Application heartbeats for software performance and health	claim
8	109	Scheduling support for transactional memory contention management	claim
9	108	GAMBIT: effective unit testing for concurrency libraries	claim
10	103	Symbolic prefetching in transactional distributed shared memory	claim
11	103	Structure-driven optimizations for amorphous data-parallel programs	claim
12	99	NOrec: streamlining STM by abolishing ownership records	claim
13	97	Model-driven autotuning of sparse matrix-vector multiply on GPUs	claim
14	96	The LOFAR correlator: implementation and performance analysis	claim
15	95	Lazy binary-splitting: a run-time adaptive work-stealing scheduler	claim
16	94	Leveraging parallel nesting in transactional memory	claim
17	94	Applying the concurrent collections programming model to asynchronous parallel dense linear algebra	claim
18	93	Modeling transactional memory workload performance	claim
19	93	Featherweight X10: a core calculus for async-finish parallelism	claim
20	92	Fast tridiagonal solvers on the GPU	claim
21	92	Debugging programs that use atomic blocks and transactional memory	claim
22	92	An adaptive performance modeling tool for GPU architectures	claim
23	91	Is transactional programming actually easier?	claim
24	88	Modeling advanced collective communication algorithms on cell-based systems	claim
25	86	Using data structure knowledge for efficient lock generation and strong atomicity	claim
26	82	Continuous speculative program parallelization in software	claim
27	82	Scaling LAPACK panel operations using parallel cache assignment	claim
28	82	Compiler aided selective lock assignment for improving the performance of software transactional memory	claim
29	80	Scalable communication protocols for dynamic sparse data exchange	claim
30	72	Helper locks for fork-join parallel programming	claim
31	65	Analyzing lock contention in multithreaded applications	claim

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