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Cache prefetching can be accomplished either by hardware or by software. [3]Hardware based prefetching is typically accomplished by having a dedicated hardware mechanism in the processor that watches the stream of instructions or data being requested by the executing program, recognizes the next few elements that the program might need based on this stream and prefetches into the processor's ...
As of 2022, data prefetching was already a common feature in CPUs, [3] but most prefetchers do not inspect the data within the cache for pointers, instead working by monitoring memory access patterns. Data memory-dependent prefetchers take this one step further.
In compiler theory, loop optimization is the process of increasing execution speed and reducing the overheads associated with loops.It plays an important role in improving cache performance and making effective use of parallel processing capabilities.
Loop tiling partitions a loop's iteration space into smaller chunks or blocks, so as to help ensure data used in a loop stays in the cache until it is reused. The partitioning of loop iteration space leads to partitioning of a large array into smaller blocks, thus fitting accessed array elements into cache size, enhancing cache reuse and eliminating cache size requirements.
The RRIP backend makes the eviction decisions. The sampled cache and OPT generator set the initial RRPV value of the inserted cache lines. Hawkeye won the CRC2 cache championship in 2017, [24] and Harmony [25] is an extension of Hawkeye which improves prefetching performance. Block diagram of the Mockingjay cache replacement policy
Loop unrolling, also known as loop unwinding, is a loop transformation technique that attempts to optimize a program's execution speed at the expense of its binary size, which is an approach known as space–time tradeoff. The transformation can be undertaken manually by the programmer or by an optimizing compiler.
For example, on early C compilers, while(1) was slower than for(;;) for an unconditional loop, because while(1) evaluated 1 and then had a conditional jump which tested if it was true, while for (;;) had an unconditional jump . Some optimizations (such as this one) can nowadays be performed by optimizing compilers. This depends on the source ...
The reason for this speedup is that in the first case, the reads of A[i][k] are in cache (since the k index is the contiguous, last dimension), but B[k][j] is not, so there is a cache miss penalty on B[k][j]. C[i][j] is irrelevant, because it can be hoisted out of the inner loop -- the loop variable there is k.