Abstract
Improving sequential performance of out-of-order
processors is becoming harder. Further improvements may require
exploitation of thread-level parallelism, on top of ILP, as
it can provide better design and performance scaling. Unfortunately,
previous “speculative multithreading” approaches have
shown small gains and/or incur a high cost, particularly for
general-purpose, non-numeric applications.
This paper investigates the fundamental limits to sequential
performance scaling through speculative multithreading - we
present an LLVM compiler-driven limit study framework that
investigates the limits of loop-level parallelism at run-time. This
new study of loop-level parallelism demonstrates the potential for
up to 4.6x and 7.2x geometric mean speedup on SpecINT2000
and SpecINT2006. Thanks to the additional consideration of
recent parallelization schemes, such as generalized DOACROSS
(HELIX), these potential speedups are higher than reported by
previous state-of-the-art limit studies.
Our analysis further categorizes the various inter-thread
dependencies and ordering constraints with respect to the specific
architectural choices and techniques each would require for
implementation. We then evaluate the relative importance of each
such constraint for different application (benchmark) types, and
provide insight into the cost/benefit trade-offs when designing
systems for efficiently implementing speculative multithreading.
Such insights should help the design of bespoke systems for speculative
multithreading while achieving better speedups, efficiency,
and scaling, relative to typical approaches which, thus far, have
relied upon adapting conventional multi-core systems.
processors is becoming harder. Further improvements may require
exploitation of thread-level parallelism, on top of ILP, as
it can provide better design and performance scaling. Unfortunately,
previous “speculative multithreading” approaches have
shown small gains and/or incur a high cost, particularly for
general-purpose, non-numeric applications.
This paper investigates the fundamental limits to sequential
performance scaling through speculative multithreading - we
present an LLVM compiler-driven limit study framework that
investigates the limits of loop-level parallelism at run-time. This
new study of loop-level parallelism demonstrates the potential for
up to 4.6x and 7.2x geometric mean speedup on SpecINT2000
and SpecINT2006. Thanks to the additional consideration of
recent parallelization schemes, such as generalized DOACROSS
(HELIX), these potential speedups are higher than reported by
previous state-of-the-art limit studies.
Our analysis further categorizes the various inter-thread
dependencies and ordering constraints with respect to the specific
architectural choices and techniques each would require for
implementation. We then evaluate the relative importance of each
such constraint for different application (benchmark) types, and
provide insight into the cost/benefit trade-offs when designing
systems for efficiently implementing speculative multithreading.
Such insights should help the design of bespoke systems for speculative
multithreading while achieving better speedups, efficiency,
and scaling, relative to typical approaches which, thus far, have
relied upon adapting conventional multi-core systems.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2021 IEEE International Symposium on Performance Analysis of Systems and Software |
| DOIs | |
| Publication status | Published - 23 Apr 2021 |