DRUT: An Efficient Turbo Boost Solution via Load Balancing in Decoupled Look-Ahead Architecture

In spite of the multicore revolution, high single thread performance still plays an important role in ensuring a decentoverall gain. Look-ahead is a proven strategy in uncoveringimplicit parallelism; however, a conventional out-of-ordercore quickly becomes resource-inefficient when looking beyond a short distance. An effective approach is to use an in-dependent look-ahead thread running on a separate contextguided by a program slice known as the skeleton. We observethat fixed heuristics to generate skeletons are often suboptimal. As a consequence, look-ahead agent is not able to targetsufficient bottlenecks to reap all the benefits it should.In this paper, we present DRUT, a holistic hardware-software solution, which achieves good single thread performance by tuning the look-ahead skeleton efficiently. First, we propose a number of dynamic transformations to branchbased code modules (we call them Do-It-Yourself or DIY)that enable a faster look-ahead thread without compromisingthe quality of the look-ahead. Second, we extend our tuningmechanism to any arbitrary code region and use a profile-driven technique to tune the skeleton for the whole program.Assisted by the aforementioned techniques, look-aheadthread improves the performance of a baseline decoupledlook-ahead by up to 1.93× with a geometric mean of 1.15×. Our techniques, combined with the weak dependence removal technique, improve the performance of a baselinelook-ahead by up to 2.12× with a geometric mean of 1.20×. This is an impressive performance gain of 1.61× over thesingle-thread baseline, which is much better compared toconventional Turbo Boost with a comparable energy budget.