Improving workload balance and code optimization on processor-in-memory systems

Chu, Slo‐Li; Huang, Tsung-Chuan; Lee, Lan-Chi

doi:10.1016/s0164-1212(02)00150-4

Cited by 2 publications

(3 citation statements)

References 13 publications

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“…However, such an approach does not exploit the real advantages of PIM architectures. This study refines our earlier SAGE (Statement-AnalysisGrouping-Evaluation) system [4,9,10] that integrates statement splitting, weight evaluation and scheduling mechanism. The original scheduling mechanism is improved to generate a superior execution schedule with a reduced time comple xity, using our new seesaw dispatching mechanism.…”

Section: Introductionsupporting

confidence: 55%

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SAGE: an automatic analyzing system for a new high-performance SoC architecture––processor-in-memory

Chu

Huang

2004

Journal of Systems Architecture

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Abstract. Continuous improvements in semiconductor fabrication density are supporting new classes of System-on-a-Chip (SoC) architectures that combine extensive processing logic/processor with high-density me mory. Such architectures are generally called Processor-in-Memory or Intelligent Memory and can support high-performance computing by reducing the performance gap between the processor and the memory. This architecture combines various processors in a single system. These processors are characterized by their computation and memory-access capabilities. Therefore, a novel strategy must be developed to identify their capabilities and dispatch the most appropriate jobs to them in order to exploit them fully. Accordingly, this study presents an automatic source-to-source parallelizing system, called SAGE, to exploit the advantages of Processor-in-Memory architectures. Unlike conventional iteration-based parallelizing systems, SAGE adopts statement-based analyzing approaches. This study a ddresses the one-host and one-memory processor configuration. The strategy of the SAGE system, in which the original program is decomposed into blocks and a feasible execution schedule is produced for the host and memory processors, is investigated as well. The experimental results for real benchmarks are also discussed.

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Section: Introductionsupporting

confidence: 55%

“…Hence, the Self-Patch Weight Evaluation scheme was designed to combine the benefits of both approaches. For a detailed description of this scheme, please refer to [4] …”

Section: Weight Evaluationmentioning

confidence: 99%

SAGE: an automatic analyzing system for a new high-performance SoC architecture––processor-in-memory

Chu

Huang

2004

Journal of Systems Architecture

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“…Workload balance is known as being a good measure of the performance of systems such as multiprocessor systems (Chu et al, 2004), software distributed shared memory systems (Zhuang et al, 2004), planning production (Riezebos et al, 2003) or health care systems (Hughes, 1999). In Lahrichi et al (2006), we analysed the impact of the demand fluctuations on the workload of the nurses in each district, and thereby concluded that in order to reduce imbalance and inequities, one should consider the possibility of assigning patients from a basic unit to nurses that are not associated with that unit.…”

Section: Workload Measurementioning

confidence: 99%

A patient assignment algorithm for home care services

Hertz

Lahrichi

2009

Journal of the Operational Research Society

109

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We consider the problem of assigning patients to nurses for home care services. The aim is to balance the workload of the nurses while avoiding long travels to visit the patients. We analyse the case of the CSSS Côte-des-Neiges, Métro and Parc Extension for which a previous analysis has shown that demand fluctuations may create work overload for the nursing staff. We propose a mixed integer programming model with some nonlinear constraints and a non-linear objective which we solve using a Tabu Search algorithm. A simplification of the workload measure leads to a linear mixed integer program which we optimize using CPLEX.

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Improving workload balance and code optimization on processor-in-memory systems

Cited by 2 publications

References 13 publications

SAGE: an automatic analyzing system for a new high-performance SoC architecture––processor-in-memory

SAGE: an automatic analyzing system for a new high-performance SoC architecture––processor-in-memory

A patient assignment algorithm for home care services

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