Instruction prefetching can effectively reduce instruction cache misses, thus improving the performance. In this paper, we propose a prefetching scheme, which employs a branch predictor to run ahead of the execution unit and to prefetch potentially useful instructions. Branch prediction-based (BP-based) prefetching has a separate small fetching unit, allowing it to compute and predict targets autonomously. Our simulations show that a 4-issue machine with BP-based prefetching achieves higher performance than a plain cache 4 times the size. In addition, BP-based prefetching outperforms other hardware instruction fetching schemes, such as next-n line prefetching and wrong-path prefetching, by a factor of 17-44% in stall overhead.

Per-address two-level branch predictors have been shown to be among the best predictors and have been implemented in current microprocessors. However, as the cycle time of modern microprocessors continues to decrease, the implementation of set-associative per-address two-level branch predictors will become more difficult. Instead, direct-mapped designs may be more attractive. In this paper, we investigate an alternative implementation of the per-address two-level predictor referred to as the tagless, direct-mapped predictor which is simpler and has faster access time. The tagless predictor can offer comparable performance to current set-associative designs since removal of tags allows more resources to be allocated for the predictor and branch target buffer (BTB). Removal of tags also decouples the per-address predictors from the BTB, thus allowing the two components to be optimized individually. Furthermore, our results show that this tagless implementation is more accurate because it handles conflict misses in the branch history table better. Finally, we examine the system cost-benefit for tagless per-address predictors across a wide design space using equal-cost contours. We study the sensitivity of performance to the workloads by comparing results from the Instruction Benchmark Suite (IBS) and SPEC CINT95. Our work provides principles and quantitative parameters for optimal configurations of such predictors.

Dynamic branch predictors are popular because they can deliver accurate branch prediction without changes to the instruction set architecture or pre existing binaries. However, to achieve the desired prediction accuracy, existing dynamic branch predictors require considerable amounts of hardware to minimize the interference effects due to aliasing in the prediction tables. We propose a new dynamic predictor, the bi mode predictor, which divides the prediction tables into two halves and by dynamically determining the current "mode" of the program, selects the appropriate half of the table for prediction. This approach is shown to preserve the merits of global history based prediction while reducing destructive aliasing and, as a result, improving prediction accuracy. Moreover, it is simple enough that it does not impact a processor's cycle time. We conclude by conducting a comprehensive study into the mechanism underlying two level dynamic predictors and investigate the criteria for their optimal designs. The analysis presented provides a general framework for studying branch predictors.

Previous branch prediction studies have relied primarily upon the SPECint89 and SPECint92 benchmarks for evaluation. Most of these benchmarks exercise a very small amount of code. As a consequence, the resources required by these schemes for accurate predictions of larger programs have not been clear. Moreover, many of these studies have simulated a very limited number of configurations. Here we report on simulations of a variety of branch prediction schemes using a set of relatively large benchmark programs that we believe to be more representative of likely system workloads. We have examined the sensitivity of these prediction schemes to variation in workload, in resources, and in design and configuration. We show that for predictors with small available resources, aliasing between distinct branches can have the dominant influence on prediction accuracy. For global history based schemes, such as GAs and gshare, aliasing in the predictor table can eliminate any advantage gained through inter branch correlation. For self-history based prediction scheme, such as PAs, it is aliasing in the buffer recording branch history, rather than the predictor table, that poses problems. Past studies have sometimes confused these effects and allocated resources incorrectly.

Seeking high branch-prediction accuracy, architects are making use of the extended history of individual branches. One approach is to divide the branch prediction task into two levels: the first records the results of previous branches; the second makes predictions based upon previous instances in which particular patterns arose. PAg predictors use simple state machines in the second level to provide adaptive predictions. We show that this adaptive level benefits from a high level of hysteresis. We further show that, if the predictions for this second level are fixed rather than adaptive, i.e. a PSg organization, the performance can be superior to that of PAg predictors for short branch histories and close to PAg performance for longer predictors. The patterns of errors among these schemes provide insight into the workings of a wide variety of two-level schemes.