CPA-CPL: Exploring and Exploiting Heterogeneous Cache Sharing in Chip Multiprocessors Systems for Locality Optimization and Proactive Cache Management

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The increasing problems of power, heat dissipation, and designcomplexity have caused a shift in processor technology to favormulticore multiprocessors. Along with that shift, the sharing ofmemory hierarchy becomes deeper, heterogeneous and more complex,causing cache contention, increased conflicts, and also, synergysharing. Without understanding the implications of this change,current multicore systems suffer from considerable performancedegradation, poor performance isolation and inferior fairnessguarantees. The urgency of these issues increases as the degree ofprocessor-level parallelism increments rapidly.Prior studies, mostly in areas of architecture and operating systems,rely on simple heuristics to estimate cache requirement of corunningprograms; the inaccuracy and overhead limits their scalability andeffectiveness. This work tackles these challenges uniquely from thecompiler aspect by constructing predictive behavior models forcorunning processes, developing cache-sharing-aware programtransformations and loop scheduling, and combining the program-levelknowledge of programming systems with the proactive resourcemanagement by runtime systems. Specifically, this work proposesinclusive reuse signatures to characterize inclusive locality---thememory behavior of corunning programs on shared caches, andinter-thread affinity models to capture data locality among parallelthreads. It tackles the challenges facing the measurement, predictionand exploitation of inclusive locality. The analysis opens newopportunities for shared-cache optimizations by both compilers andruntime systems. The PI develops a series of program transformations,such as inter-thread memory reorganization and cache-sharing awareloop scheduling, to increase inter-thread spacial locality andameliorate conflicts, contention and false sharing. For runtimesystems, this work invents proactive cache management which partitionscaches or schedules processes according to predicted inclusivelocality proactively, overcoming the limitations of current reactiveschemes on scalability, accuracy and effectiveness.