Structured dataflow analysis for arrays and its use in an optimizing complier
Software—Practice & Experience
Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
Array-data flow analysis and its use in array privatization
POPL '93 Proceedings of the 20th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Improving locality and parallelism in nested loops
Improving locality and parallelism in nested loops
Interprocedural array region analyses
International Journal of Parallel Programming - Special issue: selected papers from the eighth international workshop on languages and compilers for parallel computing
Fusion of Loops for Parallelism and Locality
IEEE Transactions on Parallel and Distributed Systems
Experience with efficient array data flow analysis for array privatization
PPOPP '97 Proceedings of the sixth ACM SIGPLAN symposium on Principles and practice of parallel programming
Data locality enhancement by memory reduction
ICS '01 Proceedings of the 15th international conference on Supercomputing
Blocking and array contraction across arbitrarily nested loops using affine partitioning
PPoPP '01 Proceedings of the eighth ACM SIGPLAN symposium on Principles and practices of parallel programming
Loop fusion for memory space optimization
Proceedings of the 14th international symposium on Systems synthesis
Space-time trade-off optimization for a class of electronic structure calculations
PLDI '02 Proceedings of the ACM SIGPLAN 2002 Conference on Programming language design and implementation
High Performance Compilers for Parallel Computing
High Performance Compilers for Parallel Computing
Structure of Computers and Computations
Structure of Computers and Computations
Collective Loop Fusion for Array Contraction
Proceedings of the 5th International Workshop on Languages and Compilers for Parallel Computing
Maximizing Loop Parallelism and Improving Data Locality via Loop Fusion and Distribution
Proceedings of the 6th International Workshop on Languages and Compilers for Parallel Computing
Better tiling and array contraction for compiling scientific programs
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
Array Placement for Storage Size Reduction in Embedded Multimedia Systems
ASAP '97 Proceedings of the IEEE International Conference on Application-Specific Systems, Architectures and Processors
Loop Alignment for Memory Accesses Optimization
Proceedings of the 12th international symposium on System synthesis
Improving Data Locality by Array Contraction
IEEE Transactions on Computers
MPSoC memory optimization using program transformation
ACM Transactions on Design Automation of Electronic Systems (TODAES)
| Hi-index | 0.00 |
Reducing memory space requirement is important to many applications. For data-intensive applications it may help avoid executing the program out-of-core. For high-performance computing, memory space reduction may improve the cache hit rate as well as performance. For embedded systems it can reduce the memory requirement, the memory latency and the energy consumption. This paper investigates program transformations which a compiler can use to reduce the memory space required for storing program data. In particular, the paper uses integer programming to model the problem of combining loop shifting, loop fusion and array contraction to minimize the data memory required to execute a collection of multi-level loop nests. The integer programming problem is then reduced to an equivalent network flow problem which can be solved in polynomial time.