This paper explores Speculative Precomputation, a technique that uses idle thread contexts in a multithreaded architecture to improve performance of single-threaded applications. It attacks program stalls from data cache misses by pre-computing future memory accesses in available thread contexts, and prefetching these data. This technique is evaluated by simulating the performance of a research processor based on the Itanium T M ISA supporting Simultaneous Multithreading. Two primary forms of Speculative Precomputation are evaluated. If only the non-speculative thread spawns speculative threads, performance gains of up to 30% are achieved when assuming ideal hardware. However, this speedup drops considerably with more realistic hardware assumptions. Permitting speculative threads to directly spawn additional speculative threads reduces the overhead associated with spawning threads and enables significantly more aggressive speculation, overcoming this limitation. Even with realistic costs for spawning threads, speedups as high as 169% are achieved, with an average speedup of 76%.

A large number of memory accesses in memory-

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Uniprocessor simulators track resource utilization cycle by cycle to estimate performance. Multiprocessor simulators, however, must account for synchronization events that increase the cost of every cycle simulated and shared resource contention that increases the total number of cycles simulated. These effects cause multiprocessor simulation times to scale superlinearly with the number of cores.Composable performance regression (CPR) fundamentally addresses these intractable multiprocessor simulation times, estimating multiprocessor performance with a combination of uniprocessor, contention, and penalty models. The uniprocessor model predicts baseline performance of each core while the contention models predict interfering accesses from other cores. Uniprocessor and contention model outputs are composed by a penalty model to produce the final multiprocessor performance estimate. Trained with a production quality simulator, CPR is accurate with median errors of 6.63, 4.83 percent for dual-, quad-core multiprocessors. Furthermore, composable regression is scalable, requiring 0.33x the simulations required by prior regression strategies.