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High load latency that results from deep cache hierarchies and relatively slow main memory is an important limiter of single-thread performance. Data prefetch helps reduce this latency by fetching data up the hierarchy before it is requested by load instructions. However, data prefetching has shown to be imperfect in many situations. We propose cache-level prediction to complement prefetchers. Our method predicts which memory hierarchy level a load will access allowing the memory loads to start earlier, and thereby saves many cycles. The predictor provides high prediction accuracy at the cost of just one cycle added latency to L1 misses. Level prediction reduces the memory access latency by 20% on average, and provides speedup of 10.3% over a conventional baseline, and 6.1% over a boosted baseline on generic, graph, and HPC applications. 

Today, larger memory capacity and higher memory bandwidth are required for better performance and energy efficiency for many important client and datacenter applications. Hardware memory compression provides a promising direction to achieve this without increasing system cost. Unfortunately, current memory compression solutions face two significant challenges. First, keeping memory compressed requires additional memory accesses, sometimes on the critical path, which can cause performance overheads. Second, they require changing the operating system to take advantage of the increased capacity, and to handle incompressible data, which delays deployment. We propose Compresso, a hardware memory compression architecture that minimizes memory overheads due to compression, with no changes to the OS. We identify new data-movement trade-offs and propose optimizations that reduce additional memory movement to improve system efficiency. We propose a holistic evaluation for compressed systems. Our results show that Compresso achieves a 1.85x compression for main memory on average, with a 24% speedup over a competitive hardware compressed system for single-core systems and 27% for multi-core systems. As compared to competitive compressed systems, Compresso not only reduces performance overhead of compression, but also increases performance gain from higher memory capacity.