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Highly requested data is cached in high-speed access memory stores, allowing swifter access by central processing unit (CPU) cores. Cache hierarchy is a form and part of memory hierarchy and can be considered a form of tiered storage. [1] This design was intended to allow CPU cores to process faster despite the memory latency of main memory access.
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Already available in literature there are extensions that take into account the impact of NUMA organization of memory, [6] of out-of-order execution, [9] of memory latencies, [9] [10] and to model at a finer grain the cache hierarchy [5] [9] in order to better understand what is actually limiting performance and drive the optimization process.
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Designing for high performance requires considering the restrictions of the memory hierarchy, i.e. the size and capabilities of each component. Each of the various components can be viewed as part of a hierarchy of memories (m 1, m 2, ..., m n) in which each member m i is typically smaller and faster than the next highest member m i+1 of the ...
To illustrate both specialization and multi-level caching, here is the cache hierarchy of the K8 core in the AMD Athlon 64 CPU. [59] Cache hierarchy of the K8 core in the AMD Athlon 64 CPU. The K8 has four specialized caches: an instruction cache, an instruction TLB, a data TLB, and a data cache. Each of these caches is specialized:
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The critical component in most high-performance computers is the cache. Since the cache exists to bridge the speed gap, its performance measurement and metrics are important in designing and choosing various parameters like cache size, associativity, replacement policy, etc. Cache performance depends on cache hits and cache misses, which are ...
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