Abstract
State-of-the-art run-time systems are a poor match to diverse, dynamic distributed applications because they are designed to provide support to a wide variety of applications, without much customization to individual specific requirements. Little or no guiding information flows directly from the application to the run-time system to allow the latter to fully tailor its services to the application. As a result, the performance is disappointing. To address this problem, we propose application-centric computing, or SMART APPLICATIONS. In the executable of smart applications, the compiler embeds most run-time system services, and a performance-optimizing feedback loop that monitors the application’s performance and adaptively reconfigures the application and the OS/hardware platform. At run-time, after incorporating the code’s input and the system’s resources and state, the SmartApp performs a global optimization. This optimization is instance specific and thus much more tractable than a global generic optimization between application, OS and hardware. The resulting code and resource customization should lead to major speedups. In this paper, we first describe the overall architecture of Smartapps and then present the achievements to date: Run-time optimizations, performance modeling, and moderately reconfigurable hardware.
Research supported in part by NSF CAREER Award CCR-9734471, NSF CAREER Award CCR-9624315, NSF Grant ACI-9872126, NSF-NGS EIA-9975018, DOE ASCI ASAP Level 2 Grant B347886, and Hewlett-Packard Equipment Grants
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Rauchwerger, L., Amato, N.M., Torrellas, J. (2001). SmartApps: An Application Centric Approach to High Performance Computing. In: Midkiff, S.P., et al. Languages and Compilers for Parallel Computing. LCPC 2000. Lecture Notes in Computer Science, vol 2017. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45574-4_6
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