Hierarchical algorithm partitioning at system level for an improved utilization of memory structures

• Published in: IEEE transactions on computer-aided design of integrated circuits and systems Vol. 18; no. 1; pp. 14 - 24
• Main Authors: Eckhardt, U., Merker, R.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: New York, NY IEEE 01.01.1999; Institute of Electrical and Electronics Engineers
• Subjects: Algorithm design and analysis; Algorithms; Applied sciences; Architecture; Arrays; Computer aided design; Costs; Design engineering; Design. Technologies. Operation analysis. Testing; Electronics; Energy consumption; Exact sciences and technology; Integrated circuits; Memory architecture; Parallel processing; Partitioning; Partitioning algorithms; Processor scheduling; Registers; Reuse; Scheduling; Scheduling algorithm; Semiconductor electronics. Microelectronics. Optoelectronics. Solid state devices; System performance; Circuit architecture; Performance evaluation; Partitioning; Memory; Codesign; Integrated circuit; Parallel processing; Hierarchical system; Algorithm; Computer aided design
• ISSN: 0278-0070; 1937-4151
• DOI: 10.1109/43.739055

The object of algorithm design in context with a hierarchically structured memory system is a reduction of access cycles to higher memory levels by an increase of data reuse from levels closer to execution units. The object of our approach is to systematically construct an algorithm coding, starting from a weak single assignment form, so that parameters of the algorithm code (number and type of partitions, scheduling orders) can be directly mapped on parameters of the architecture (number of memory levels, size of the memories, input/output access behavior) and vice versa. Target architectures are processors with from one up to a few execution units and with a hierarchically structured memory system. The approach is based on methods derived from the realm of array synthesis and consists of a recursively defined algorithm partitioning. An approach to a quantitative determination of data reuse in recursively partitioned algorithms is given.