Optimizing stack frame accesses for processors with restricted addressing modes
Software—Practice & Experience
Storage assignment to decrease code size
PLDI '95 Proceedings of the ACM SIGPLAN 1995 conference on Programming language design and implementation
Algorithms for address assignment in DSP code generation
Proceedings of the 1996 IEEE/ACM international conference on Computer-aided design
Analysis and evaluation of address arithmetic capabilities in custom DSP architectures
DAC '97 Proceedings of the 34th annual Design Automation Conference
DSP address optimization using a minimum cost circulation technique
ICCAD '97 Proceedings of the 1997 IEEE/ACM international conference on Computer-aided design
MediaBench: a tool for evaluating and synthesizing multimedia and communicatons systems
MICRO 30 Proceedings of the 30th annual ACM/IEEE international symposium on Microarchitecture
Advanced compiler design and implementation
Advanced compiler design and implementation
A uniform optimization technique for offset assignment problems
Proceedings of the 11th international symposium on System synthesis
Storage assignment optimizations to generate compact and efficient code on embedded DSPs
Proceedings of the ACM SIGPLAN 1999 conference on Programming language design and implementation
Address code generation for digital signal processors
Proceedings of the 38th annual Design Automation Conference
Code generation for embedded processors
ISSS '00 Proceedings of the 13th international symposium on System synthesis
Code Optimization Techniques for Embedded Processors: Methods, Algorithms, and Tools
Code Optimization Techniques for Embedded Processors: Methods, Algorithms, and Tools
Improving Offset Assignment for Embedded Processors
LCPC '00 Proceedings of the 13th International Workshop on Languages and Compilers for Parallel Computing-Revised Papers
Optimal Live Range Merge for Address Register Allocation in Embedded Programs
CC '01 Proceedings of the 10th International Conference on Compiler Construction
Storage assignment optimizations through variable coalescence for embedded processors
Proceedings of the 2003 ACM SIGPLAN conference on Language, compiler, and tool for embedded systems
Code generation and optimization for embedded digital signal processors
Code generation and optimization for embedded digital signal processors
Simple offset assignment in presence of subword data
Proceedings of the 2003 international conference on Compilers, architecture and synthesis for embedded systems
Reducing code size through address register assignment
ACM Transactions on Embedded Computing Systems (TECS)
Offset assignment using simultaneous variable coalescing
ACM Transactions on Embedded Computing Systems (TECS)
Evaluation of offset assignment heuristics
HiPEAC'07 Proceedings of the 2nd international conference on High performance embedded architectures and compilers
Offset assignment showdown: evaluation of DSP address code optimization algorithms
CC'03 Proceedings of the 12th international conference on Compiler construction
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Most modern digital signal processors (DSPs) provide multiple address registers and a dedicated address generation unit (AGU) which performs address generation in parallel to instruction execution. There is no address computation overhead if the next address is within the auto-modify range. A careful placement of variables in memory is utilized to decrease the number of address arithmetic instructions and thus to generate compact and efficient code. The simple offset assignment (SOA) problem concerns the layout of variables for machines with one address register and the general offset assignment (GOA) deals with multiple address registers. Both these problems assume that each variable needs to be allocated for the entire duration of a program. Both SOA and GOA are NP-complete. In this article, we present effective heuristics for the simple and the general offset assignment problems with variable coalescing where two or more non-interfering variables can be mapped into the same memory location. Results on several benchmarks show the significant improvement of our proposed heuristics compared to other heuristics in the literature.