A Run-Time Program Phase Detection Technique For Optimizing Per-Phase L2 Cache Demand

Ibrahim Ziedan, Shaymaa Mahmoud Serag, Hazem Shehata


Understanding program behavior is at the foundation of computer architecture and program optimization. Programs pass through different behaviors where their performance characteristics and hardware resource requirements vary. Program phase detection and classification research aiming to understand the program time-varying behavior, can unlock a lot of phase-based optimizations which are specially tailored to improve the performance of each individual program phase. In this paper, we introduce an efficient run-time phase detection and classification technique, based on tracking changes in the L2 cache access pattern of different portions in the program execution. The proposed technique monitors a running program and keeps track what phase the running program is currently executing, with no need to recompile the tracked program, and with execution time overhead of 4%, on average. Performance Monitoring Unit (PMU) is exploited to sample the memory addresses causing L1 data cache misses. This profiling data is used to construct the Cache Access Signature Vectors (CASVs) that accurately reflect the L2 cache access patterns for each interval of execution. By comparing CASVs, the proposed technique classifies the program into a set of stable phases with high degree of intra-phase homogeneity. Our evaluation shows that phase changes detected by our technique have strong correlation with the variation in Instruction Per Cycle (IPC). Furthermore, our technique can contribute in reducing L2 cache miss rates, and optimizing L2 cache utilization, through its direct capability of estimating per-phase L2 cache demand


Program Phase Detection Basic Block Vectors Working Set Signatures Phase-based Optimization Cache Access Pattern Performance Monitoring Unit

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