Alternative implementations of two-level adaptive branch prediction

As the issue rate and depth of pipelining of high performance Superscalar processors increase, the importance of an excellent branch predictor becomes more vital to delivering the potential performance of a wide-issue, deep pipelined microarchitecture. We propose a new dynamic branch predictor (Two-Level Adaptive Branch Prediction) that achieves substantially higher accuracy than any other scheme reported in the literature. The mechanism uses two levels of branch history information to make predictions, the history of the last L branches encountered, and the branch behavior for the last s occurrences of the specific pattern of these k branches. We have identified three variations of the Two-Level Adaptive Branch Prediction, depending on how finely we resolve the history information gathered. We compute the hardware costs of implementing each of the three variations, and use these costs in evaluating their relative effectiveness. We measure the branch prediction accuracy of the three variations of Two-Level Adaptive Branch Prediction, along with several other popular proposed dynamic and static prediction schemes, on the SPEC benchmarks. We show that the average prediction accuracy for TwoLevel Adaptive Branch Prediction is 97 percent, while the other known schemes achieve at most 94.4 percent average prediction accuracy. We measure the effectiveness of different prediction algorithms and different amounts of history and pattern information. We measure the costs of each variation to obtain the same prediction accuracy.

Bottleneck identification and scheduling in multithreaded applications

Abstract Performance of multithreaded applications is limited by a variety of bottlenecks, e.g. critical sections, barriers and slow pipeline stages. These bottlenecks serialize execution, waste valuable execution cycles, and limit scalability of applications. This paper proposes Bottleneck Identification and Scheduling (BIS), a cooperative software-hardware mechanism to identify and accelerate the most critical bottlenecks. BIS identifies which bottlenecks are likely to reduce performance by measuring the number of cycles threads have to wait for each bottleneck, and accelerates those bottlenecks using one or more fast cores on an Asymmetric Chip MultiProcessor (ACMP). Unlike previous work that targets specific bottlenecks, BIS can identify and accelerate bottlenecks regardless of their type. We compare BIS to four previous approaches and show that it outperforms the best of them by 15% on average. BIS' performance improvement increases as the number of cores and the number of fast cores in the system increase

A comparison of dynamic branch predictors that use two levels of branch history

Recent attention to speculative execution as a mechanism for increasing performance of single instruction streams has demanded substantially better branch prediction than what has been previously available. We [1, 2] and Pan, So, and Rahmeh [4] have both proposed variations of the same aggressive dynamic branch predictor for handling those needs. We call the basic model Two-Level Adaptive Branch Prediction � Pan, So, and Rahmeh call it Correlation Branch Prediction. In this paper, we adopt the terminology of [2] and show that there arereally nine variations of the same basic model. We compare the nine variations with respect to the amount of history information kept. We study the e ects of di erent branch history lengths and pattern history table con gurations. Finally, we evaluate the cost e ectiveness of the nine variations.