Advanced compiler optimizations for sparse computations

Regular data dependence checking on sparse codes usually results in very conservative estimates of actual dependences that will occur at run-time. Clearly, this is caused by the usage of compact data structures that are necessary to exploit sparsity in order to reduce storage requirements and comput...

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Bibliographic Details
Main Authors: Bik, A. J. C., Wijshoff, H. A. G.
Format: Conference Proceeding
Language:English
Subjects:
Computer science
Computing methodologies > Symbolic and algebraic manipulation > Symbolic and algebraic algorithms > Linear algebra algorithms
Concurrent computing
Data structures
High performance computing
Information analysis
Mathematics of computing > Mathematical analysis > Numerical analysis > Computations on matrices
Optimizing compilers
Parallel processing
Program processors
Runtime
Software and its engineering > Software notations and tools > Compilers
Software and its engineering > Software notations and tools > Compilers > Source code generation
Software and its engineering > Software notations and tools > General programming languages > Language features > Data types and structures
Sparse matrices
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Summary:Regular data dependence checking on sparse codes usually results in very conservative estimates of actual dependences that will occur at run-time. Clearly, this is caused by the usage of compact data structures that are necessary to exploit sparsity in order to reduce storage requirements and computational time. However, if the compiler is presented with dense code and automatically converts it into code that operates on sparse data structures, then the dependence information obtained by analysis on the original code can be used to exploit potential concurrency in the generated code. The authors present synchronization generating and manipulating techniques that are based on this concept.
ISSN:1063-9535
DOI:10.1145/169627.169765