PPOPP 2010 - PDF Preprint Papers

PPOPP 2010 Principles and Practice of Parallel Programming

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PPOPP
2010
ACM

353views Distributed and Parallel Com...» more PPOPP 2010»

Data transformations enabling loop vectorization on multithreaded data parallel architectures

13 years 9 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	353	Data transformations enabling loop vectorization on multithreaded data parallel architectures	claim
2	308	Thread to strand binding of parallel network applications in massive multi-threaded systems	claim
3	285	A distributed placement service for graph-structured and tree-structured data	claim
4	284	A practical concurrent binary search tree	claim
5	259	An adaptive performance modeling tool for GPU architectures	claim
6	240	Load balancing on speed	claim
7	234	Analyzing lock contention in multithreaded applications	claim
8	232	Does cache sharing on modern CMP matter to the performance of contemporary multithreaded programs?	claim
9	223	Application heartbeats for software performance and health	claim
10	222	Scaling LAPACK panel operations using parallel cache assignment	claim
11	216	Structure-driven optimizations for amorphous data-parallel programs	claim
12	210	Scheduling support for transactional memory contention management	claim
13	209	Model-driven autotuning of sparse matrix-vector multiply on GPUs	claim
14	203	GAMBIT: effective unit testing for concurrency libraries	claim
15	202	Lazy binary-splitting: a run-time adaptive work-stealing scheduler	claim
16	202	Applying the concurrent collections programming model to asynchronous parallel dense linear algebra	claim
17	199	Symbolic prefetching in transactional distributed shared memory	claim
18	196	The LOFAR correlator: implementation and performance analysis	claim
19	194	NOrec: streamlining STM by abolishing ownership records	claim
20	191	Scalable communication protocols for dynamic sparse data exchange	claim
21	189	Leveraging parallel nesting in transactional memory	claim
22	179	Modeling transactional memory workload performance	claim
23	171	Featherweight X10: a core calculus for async-finish parallelism	claim
24	171	Debugging programs that use atomic blocks and transactional memory	claim
25	167	Using data structure knowledge for efficient lock generation and strong atomicity	claim
26	166	Continuous speculative program parallelization in software	claim
27	158	Is transactional programming actually easier?	claim
28	156	Modeling advanced collective communication algorithms on cell-based systems	claim
29	154	Fast tridiagonal solvers on the GPU	claim
30	141	Compiler aided selective lock assignment for improving the performance of software transactional memory	claim
31	140	Helper locks for fork-join parallel programming	claim

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