As multicore systems become the dominant mainstream computing technology, one of the most difficult challenges the industry faces is the software. Applications with large amounts of explicit thread-level parallelism naturally scale performance with the number of cores, but single-threaded applications realize little to no gains with additional cores. One solution to this problem is automatic parallelization that frees the programmer from the difficult task of parallel programming and offers hope for handling the vast amount of legacy single-threaded software. There is a long history of automatic parallelization for scientific applications, but the techniques have generally failed in the context of general-purpose software. Thread-level speculation overcomes the problem of memory dependence analysis by speculating unlikely dependences that serialize execution. However, this approach has lead to only modest performance gains. In this paper, we take another look at exploiting loop-level parallelism in single-threaded applications. We show that substantial amounts of loop-level parallelism is available in general-purpose applications, but it lurks beneath the surface and is often obfuscated by a small number of data and control dependences. We adapt and extend several code transformations from the instruction-level and scientific parallelization communities to uncover the hidden parallelism. Our results show that 61% of the dynamic execution of studied benchmarks can be parallelized with our techniques compared to 27% using traditional thread-level speculation techniques, resulting in a speedup of 1.84 on a four core system compared to 1.41 without transformations.

Chip multiprocessors with multiple simpler cores are gaining popularity because they have the potential to drive future performance gains without exacerbating the problems of power dissipation and complexity. Current chip multiprocessors increase throughput by utilizing multiple cores to perform computation in parallel. These designs provide real benefits for server-class applications that are explicitly multi-threaded. However, for desktop and other systems where single-thread applications dominate, multicore systems have yet to offer much benefit. Chip multiprocessors are most efficient at executing coarse-grain threads that have little communication. However, general-purpose applications do not provide many opportunities for identifying such threads, due to frequent use of pointers, recursive data structures, if-then-else branches, small function bodies, and loops with small trip counts. To attack this mismatch, this paper proposes a multicore architecture, referred to as Voltron that extends traditional multicore systems in two ways. First, it provides a dual-mode scalar operand network to enable efficient inter-core communication and lightweight synchronization. Second, Voltron can organize the cores for execution in either coupled or decoupled mode. In coupled mode, the cores execute multiple instruction streams in lock-step to collectively function as a wide-issue VLIW. In decoupled mode, the cores execute a set of fine-grain communicating threads extracted by the compiler. This paper describes the Voltron architecture and associated compiler support for orchestrating bi-modal execution