TransPlant: A parameterized methodology for generating transactional memory workloads

Transactional memory provides a means to bridge the discrepancy between programmer productivity and the difficulty in exploiting thread-level parallelism gains offered by emerging chip multiprocessors. Because the hardware has outpaced the software, there are very few modern multithreaded benchmarks available and even fewer for transactional memory researchers. This hurdle must be overcome for transactional memory research to mature and to gain widespread acceptance. Currently, for performance evaluations, most researchers rely on manually converted lock-based multithreaded workloads or the small group of programs written explicitly for transactional memory. Using converted benchmarks is problematic because they have been tuned so well that they may not be representative of how a programmer will actually use transactional memory. Hand coding stressor benchmarks is unattractive because it is tedious and time consuming. A new parameterized methodology that can automatically generate a program based on the desired high-level program characteristics benefits the transactional memory community. In this work, we propose techniques to generate parameterized transactional memory benchmarks based on a feature set, decoupled from the underlying transactional model. Using principle component analysis, clustering, and raw transactional performance metrics, we show that TransPlant can generate benchmarks with features that lie outside the boundary occupied by these traditional benchmarks. We also show how TransPlant can mimic the behavior of SPLASH-2 and STAMP transactional memory workloads. The program generation methods proposed here will help transactional memory architects select a robust set of programs for quick design evaluations.