Superoptimizer: a look at the smallest program
ASPLOS II Proceedings of the second international conference on Architectual support for programming languages and operating systems
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Advanced compiler optimizations for sparse computations
Journal of Parallel and Distributed Computing
Reduction of Cache Coherence Overhead by Compiler Data Layout and Loop Transformation
Proceedings of the Fourth International Workshop on Languages and Compilers for Parallel Computing
Data Compression Transformations for Dynamically Allocated Data Structures
CC '02 Proceedings of the 11th International Conference on Compiler Construction
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In modern computers, a single “random” access to main memory often takes as much time as executing hundreds of instructions. Rather than using traditional compiler approaches to enhance locality by interchanging loops, reordering data structures, etc., this paper proposes the radical concept of using aggressive data compression technology to improve hierarchical memory performance by reducing memory address reference entropy. In some cases, conventional compression technology can be adapted. However, where variable access patterns must be permitted, other compression techniques must be used. For the special case of random access to elements of sparse matrices, data structures and compiler technology already exist. Our approach is much more general, using compressive hash functions to implement random access lookup tables. Techniques that can be used to improve the effectiveness of any compression method in reducing memory access entropy also are discussed.