Efficacy and performance impact of value prediction
Bohuslav Rychlik, John Faistl, Bryon Krug, John Paul Shen
Abstract
Bohuslav Rychlik, John Faistl, Bryon Krug, John Paul Shen
Abstract
this paper are generated by an execution-driven performance simulator [2]. The simulator uses a cycle-accurate machine model based on the PowerPC 604 [3][6][17]. All key aspects of the PowerPC 604 microarchitecture are modeled. The SPEC95 benchmark set is used for this work, including eight integer and six floating point programs. The benchmark programs, input sets, and run lengths are summarized in Table 1. To reduce simulation times, we use small input files and limit benchmark length to 100 million instructions. All user library calls are modeled, though system calls are not.
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this paper are generated by an execution-driven performance simulator [2]. The simulator uses a cycle-accurate machine model based on the PowerPC 604 [3][6][17]. All key aspects of the PowerPC 604 microarchitecture are modeled. The SPEC95 benchmark set is used for this work, including eight integer and six floating point programs. The benchmark programs, input sets, and run lengths are summarized in Table 1. To reduce simulation times, we use small input files and limit benchmark length to 100 million instructions. All user library calls are modeled, though system calls are not.
Key concepts: Computer science, Branch predictor, Speedup, Spec#, Value (mathematics), Performance prediction, Parallel computing, Superscalar