PERFORMANCE CHARACTERIZATION OF CACHE REPLACEMENT STRATEGIES FOR MANAGING SHARED LLC OVER HETEROGENEOUS INTEGRATED CPU-GPU ARCHITECTURE USING MULTI-THREADED WORKLOAD

he CPU is mostly designed for sequential and complex decision-making. it propagates with its instruction processing in a few pipeline stages, including steps for data and instruction fetching from memory. With time, the GPU, with its parallel processing capabilities, came into existence to assist the CPU, mainly for graphics processing. Because of parallel processing and a huge number of thread executions, its data access pattern is quite different from that of CPUs. In its rendering pipelines, the GPU may access varied data streams. The access may vary in semantics in different access patterns. Due to commercial requirements and to minimize data sharing latency between CPU and GPU, integrated processor designs are emerging. these designs accommodate both CPU and GPU in a single chip, sharing common resources. On many occasions, sharing these resources becomes a bottleneck for the whole system, deteriorating overall performance. Last Level Cache sharing among CPU and GPU is a bigger challenge in this design approach. A suitable cache eviction strategy is highly essential to utilize the shared LLC space effectively. Optimized cache with better clock speed can also be considered along with a higher configuration for CPU and GPU to improve the overall performance. Here in our work, we have designed an integrated heterogeneous CPU-GPU model with upgraded configurations to boost performance. Further, we have compared the performance with Alder Lake and Raptor Lake processors, taking them as baselines. We have achieved a speedup improvement of 59.8% and 22.12% compared to Alder Lake and Raptor Lake processors, respectively, taking the geometric mean of eight different sets of workloads. Eight different cache replacement schemes have been configured in MacSim simulator, and the workload from different benchmark suites has been given. Through simulation results, we found an average read miss count improvement of 29.41% and 15.38% over Alder Lake and Raptor Lake, respectively. This may help to IT infrastructure in terms of actuarial science, real time systems etc