Program optimization strategies for data parallel many core processors

This creates a need for structured and automatable optimization techniques that are capable of finding a near-optimal program configuration for this new class of architecture. My work discusses various strategies for optimizing programs on a highly dataparallel architecture with fine-grained sharing of resources. I first investigate useful strategies in optimizing a suite of applications.

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Abstract With the development of high-performance computing and big data applications, the scale of data transmitted, stored, and processed by high-performance computing cluster systems is increasing explosively. Introduction With the improvement of high-performance computer performance, its scale is expanding. This also requires efficient compression algorithms to reduce the amount of data storage and processing of algorithms such as distributed big data processing and machine learning [ 2 , 3 ] Lossless compression algorithms have a wide variety of open-source implementations.

Figure 1. Figure 2. Figure 3. Figure 4. Table 1. Dictionary initialization 2. Encode the maximum string as offset , len , cur from current position 7. Encode the value as 0, 0, cur according to the current position 9.

Algorithm 1. Algorithm 2. Figure 5. Figure 6. Table 2. Item Parameters MPE 1. Table 3. Figure 7. Figure 8. Table 4. Hardware architecture Intel E v2 3. Table 5. Performance test comparison between Intel x86 and Sunway References N. Deepa, Q. Pham, D. Nguyen et al. View at: Google Scholar G. Reddy, M. Reddy, K. Lakshmanna et al.

Rajadurai, M. Alazab, N. Kumar, and T. Pankratius, A. Jannesari, and W. Gristwood, P. Fineran, L. Everson, and G.

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Skip to main content. This service is more advanced with JavaScript available. Advertisement Hide. Conference paper First Online: 11 August This is a preview of subscription content, log in to check access. Allen, M. Soft Matter-From Synthet. Haile, J.