With the emergence of multicore processors, various aggressive execution models have been proposed to exploit fine-grained thread-level parallelism, taking advantage of the fast on-chip interconnection communication. However, the aggressive nature of these execution models often leads to excessive energy consumption incommensurate to execution time reduction. In the context of Thread-Level Speculation, we demonstrated that on a same-ISA heterogeneous multicore system, by dynamically deciding how on-chip resources are utilized, speculative threads can achieve performance gain in an energy-efficient way. Through a systematic design space exploration, we built a multicore architecture that integrates heterogeneous components of processing cores and first-level caches. To cope with processor reconfiguration overheads, we introduced runtime mechanisms to mitigate their impacts. To match program execution with the most energy-efficient processor configuration, the system was equipped with a dynamic resource allocation scheme that characterizes program behaviors using novel processor counters. We evaluated the proposed heterogeneous system with a diverse set of benchmark programs from SPEC CPU2000 and CPU20006 suites. Compared to the most efficient homogeneous TLS implementation, we achieved similar performance but consumed 18% less energy. Compared to the most efficient homogeneous uniprocessor running sequential programs, we improved performance by 29% and reduced energy consumption by 3.6%, which is a 42% improvement in energy-delay-squared product.

Efficiently utilizing multicore processors to improve their performance potentials demands extracting thread-level parallelism from the applications. Various novel and sophisticated execution models have been proposed to extract thread-level parallelism from sequential programs. One such execution model, Thread-Level Speculation (TLS), allows potentially dependent threads to execute speculatively in parallel. However, TLS execution is inherently unpredictable, and consequently incorrect speculation could degrade performance for the multicore systems. Existing approaches have focused on using the compilers to select sequential program regions to apply TLS. Our research shows that even the state-of-the-art compiler makes suboptimal decisions, due to the unpredictability of TLS execution. Thus, we propose to dynamically optimize TLS performance. This article describes the design, implementation, and evaluation of a runtime thread dispatching mechanism that adjusts the behaviors of speculative threads based on their efficiency. In the proposed system, speculative threads are monitored by hardware-based performance counters and their performance impact is evaluated with a novel methodology that takes into account various unique TLS characteristics. Thread dispatching policies are devised to adjust the behaviors of speculative threads accordingly. With the help of the runtime evaluation, where and how to create speculative threads is better determined. Evaluated with all the SPEC CPU2000 benchmark programs written in C, the dynamic dispatching system outperforms the state-of-the-art compiler-based thread management techniques by 9.4% on average. Comparing to sequential execution, we achieve 1.37X performance improvement on a four-core CMP-based system.

Thread-level parallelism at the chip level is critical in overcoming some of the challenges that have been ushered in through the advent of modern multicore processors (CMP). Extracting speculatively parallel threads from sequential applications and executing these threads on multicore processors is a promising technique to speed up these applications on multicore systems. However, the potential degradation in energy efficiency associated is an important factor that hinders the deployment of this technique. For multicore systems that integrate same-ISA heterogeneous cores, it is possible to judiciously allocate speculative threads to achieve energy-efficient performance improvement.

It is well known that a large fraction of applications cannot be parallelized at compile time due to unpredictable data dependences such as indirect memory accesses and/or memory accesses guarded by data-dependent conditional statements. A significant body of prior work attempts to parallelize such applications using runtime data-dependence analysis and scheduling. Performance is highly dependent on the ratio of the dependence analysis overheads with respect to the actual amount of parallelism available in the code. We have found that the overheads are often high and the available parallelism is often low when evaluating applications on a modern multicore processor. We propose a novel software-based approach called dependence-aware scheduling to parallelize loops with unknown data dependences. Unlike prior work, our main goal is to reduce the negative impact of dependence computation, such that when there is not an opportunity of getting speedup, the code can still run without much slowdown. If there is an opportunity, dependence-aware scheduling is able to yield very impressive speedup. Our results indicate that dependence-aware scheduling can greatly improve performance, with up to 4x speedups, for a number of computation intensive applications. Furthermore, the results also show negligible slowdowns in a stress test, where parallelism is continuously detected but not exploited.

In response to the emergence of multicore processors, various novel and sophisticated execution models have been introduced to fully utilize these processors. One such execution model is Thread-Level Speculation (TLS), which allows potentially dependent threads to execute speculatively in parallel. While TLS offers significant performance potential for applications that are otherwise non-parallel, extracting efficient speculative threads in the presence of complex control flow and ambiguous data dependences is a real challenge. This task is further complicated by the fact that the performance of speculative threads is often architecture-dependent, input-sensitive, and exhibits phase behaviors. Thus we propose dynamic performance tuning mechanisms that determine where and how to create speculative threads at runtime.

Computer industry has adopted multi-threaded and multi-core architectures as the clock rate increase stalled in early 2000psilas. However, because of the lack of compilers and other related software technologies, most of the general-purpose applications today still cannot take advantage of such architectures to improve their performance. Thread-level speculation (TLS) has been proposed as a way of using these multi-threaded architectures to parallelize general-purpose applications. Both simultaneous multithreading (SMT) and chip multiprocessors (CMP) have been extended to implement TLS. While the characteristics of SMT and CMP have been widely studied under multi-programmed and parallel workloads, their behavior under TLS workload is not well understood. The TLS workload due to speculative nature of the threads which could potentially be rollbacked and due to variable degree of parallelism available in applications, exhibits unique characteristics which makes it different from other workloads. In this paper, we present a detailed study of the performance, power consumption and thermal effect of these multithreaded architectures against that of a Superscalar with equal chip area. A wide spectrum of design choices and tradeoffs are also studied using commonly used simulation techniques. We show that the SMT based TLS architecture performs about 21% better than the best CMP based configuration while it suffers about 16% power overhead. In terms of Energy-Delay-Squared product (ED2), SMT based TLS performs about 26% better than the best CMP based TLS configuration and 11% better than the superscalar architecture. But the SMT based TLS configuration, causes more thermal stress than the CMP based TLS architectures.