CSR---AES: Program Phase Detection and Exploitation

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Studies of application behavior reveal the nested repetition of large and small program phases, with significant variation among phases in such characteristics as memory reference patterns, memory and energy usage, I/O activity, and occupancy of micro-architectural resources. Reliably predicting and exploiting phased behavior can allow an advanced execution system to allocate resources in a way that better matches program needs, or to transform programs so that their needs better match the available resources. This project will develop the technology required to accurately detect and exploit phased behavior in a wide range of applications. Specifically, the project will combine hardware and run-time monitoring mechanisms, off-line trace analysis, and profile driven adaptation. The proposed research will evolve a more general-purpose suite of detection mechanisms, comparing alternative approaches and reconciling conflicting indications of phase boundaries. Once program phases have been detected and marked, the proposed research will investigate three principal techniques to optimize program performance. Data reorganization will improve memory hierarchy performance by reducing latency and increasing effective memory bandwidth. In particular, phase-based array regrouping and structure splitting will allow applications to better utilize the cache and minimize direct access to memory. The system will include a compiler for program transformation and a run-time monitoring system based on advanced hardware support.Scheduling and task assignment will serve to minimize load imbalance and communication bandwidth requirements. Relative loads among processes in data-parallel applications commonly vary from one phase to the next. Characterization of these loads will enable both static and dynamic load balancing. Identification of memory reference patterns will also allow locality-sensitive assignment of tasks, thereby increasing CPU utilization while decreasing bandwidth requirements. I/O prefetching will exploit phase-specific patterns of access to disk data, thereby improving disk utilization and minimizing time lost waiting for I/O. Profile-driven algorithms will build on the PIs' prior work in on-line content delivery. The proposed techniques will be incorporated into popular run-time systems.