This work aims to analyze the impacts of cache configurations on miss rates
of big data benchmarks with varying level 1 instruction (L1I) and data (L1D)
caches using the gem5 simulator. The cache miss rate of nine big data
applications from four benchmark suits is analyzed with different cache
configurations, such as increasing the cache size, varying the
associativity, and altering the line size. The gem5 provides a versatile
platform for conducting detailed experiments. The study sheds light on the
relationship between cache and big data workloads, thus offering insights
into optimizing cache configurations? effect on miss rates for improved
performance.