Unifying data and control transformations for distributed shared-memory machines
PLDI '95 Proceedings of the ACM SIGPLAN 1995 conference on Programming language design and implementation
Improving data locality with loop transformations
ACM Transactions on Programming Languages and Systems (TOPLAS)
Improving locality using loop and data transformations in an integrated framework
MICRO 31 Proceedings of the 31st annual ACM/IEEE international symposium on Microarchitecture
Proceedings of the ACM SIGPLAN 1999 conference on Programming language design and implementation
A dynamic locality optimization algorithm for linear algebra codes
Proceedings of the 2001 ACM symposium on Applied computing
Custom Memory Management Methodology: Exploration of Memory Organisation for Embedded Multimedia System Design
High Performance Compilers for Parallel Computing
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Integrating Loop and Data Transformations for Global Optimisation
PACT '98 Proceedings of the 1998 International Conference on Parallel Architectures and Compilation Techniques
Quasidynamic Layout Optimizations for Improving Data Locality
IEEE Transactions on Parallel and Distributed Systems
A Constraint Network Based Approach to Memory Layout Optimization
Proceedings of the conference on Design, Automation and Test in Europe - Volume 2
Optimization of memory system in real-time embedded systems
ICCOMP'07 Proceedings of the 11th WSEAS International Conference on Computers
MPADS: memory-pooling-assisted data splitting
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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Microprocessors & Microsystems
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Compiler technology is becoming a key component in the design of embedded systems, mostly due to increasing participation of software in the design process. Meeting system-level objectives usually requires flexible and retargetable compiler optimizations that can be ported across a wide variety of architectures. In particular, source-level compiler optimizations aiming at increasing locality of data accesses are expected to improve the quality of the generated code. Previous compiler-based approaches to improving locality have mainly focused on determining optimal memory layouts that remain in effect for the entire execution of an application. For large embedded codes, however, such static layouts may be insufficient to obtain acceptable performance. The selection of memory layouts that dynamically change over the course of a program's execution adds another dimension to data locality optimization. This paper presents a technique that can be used to automatically determine which layouts are most beneficial over specific regions of a program while taking into account the added overhead of dynamic (run time) layout changes. The results obtained using two benchmark codes show that such a dynamic approah brings significant benefits over a static state-of-the-art technique.