Multi-threading and one-sided communication in parallel LU factorization

Dense LU factorization has a high ratio of computation to communication and, as evidenced by the High Performance Linpack (HPL) benchmark, this property makes it scale well on most parallel machines. Nevertheless, the standard algorithm for this problem has non-trivial dependence patterns which limi...

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Bibliographic Details
Published in:Proceedings of the 2007 ACM/IEEE Conference on Supercomputing pp. 1 - 10
Main Authors: Husbands, Parry, Yelick, Katherine
Format: Conference Proceeding
Language:English
Published: New York, NY, USA ACM 10.11.2007
IEEE
Series:ACM Conferences
Subjects:
Communication system control
Computing methodologies > Concurrent computing methodologies > Concurrent programming languages
Concurrent computing
Costs
Delay
dense linear algebra
Government
High performance computing
latency tolerance
Multithreading
Parallel processing
Software and its engineering > Software notations and tools > General programming languages > Language types > Concurrent programming languages
Sparse matrices
Yarn
dense linear algebra
multithreading
latency tolerance
ISBN:1595937641, 9781595937643
Online Access:Get full text
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Summary:Dense LU factorization has a high ratio of computation to communication and, as evidenced by the High Performance Linpack (HPL) benchmark, this property makes it scale well on most parallel machines. Nevertheless, the standard algorithm for this problem has non-trivial dependence patterns which limit parallelism, and local computations require large matrices in order to achieve good single processor performance. We present an alternative programming model for this type of problem, which combines UPC's global address space with lightweight multithreading. We introduce the concept of memory-constrained lookahead where the amount of concurrency managed by each processor is controlled by the amount of memory available. We implement novel techniques for steering the computation to optimize for high performance and demonstrate the scalability and portability of UPC with Teraflop level performance on some machines, comparing favourably to other state-of-the-art MPI codes.
ISBN:1595937641
9781595937643
DOI:10.1145/1362622.1362664