The Community for Technology Leaders
RSS Icon
Issue No.04 - April (1994 vol.43)
pp: 431-442
<p>Detecting independent operations is a prime objective for computers that are capable of issuing and executing multiple operations simultaneously. The number of instructions that are simultaneously examined for detecting those that are independent is the scope of concurrency detection. The authors present an analytical model for predicting the performance impact of varying the scope of concurrency detection as a function of available resources, such as number of pipelines in a superscalar architecture. The model developed can show where a performance bottleneck might be: insufficient resources to exploit discovered parallelism, insufficient instruction stream parallelism, or insufficient scope of concurrency detection. The cost associated with speculative execution is examined via a set of probability distributions that characterize the inherent parallelism in the instruction stream. These results were derived using traces from a Multiflow TRACE SCHEDULING compacting FORTRAN 77 and C compilers. The experiments provide misprediction delay estimates for 11 common application-level benchmarks under scope constraints, assuming speculative, out-of-order execution and run time scheduling. The throughput prediction of the analytical model is shown to be close to the measured static throughput of the compiler output.</p>
program compilers; scheduling; concurrency control; parallel programming; performance evaluation; instruction window size; trade-offs; characterization; program parallelism; concurrency detection; performance impact; performance bottleneck; parallelism; instruction stream parallelism; probability distributions; inherent parallelism; Multiflow TRACE SCHEDULING; compilers; delay estimates; scope constraints; run time scheduling; throughput prediction.
G.B. Adams, III, P.K. Dubey, "Instruction Window Size Trade-Offs and Characterization of Program Parallelism", IEEE Transactions on Computers, vol.43, no. 4, pp. 431-442, April 1994, doi:10.1109/12.278481
285 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool