A compiler framework for speculative optimizations

Abstract

, PEN-CHUNG YEW and ROY DZ-CHING JU, TIN-FOOK NGAI, SUN CHAN ________________________________________________________________________ Speculative execution, such as control speculation or data speculation, is an effective way to improve program performance. Using edge/path profile information or simple heuristic rules, existing compiler frameworks can adequately incorporate and exploit control speculation. However, very little has been done so far to allow existing compiler frameworks to incorporate and exploit data speculation effectively in various program transformations beyond instruction scheduling. This paper proposes a speculative SSA form to incorporate information from alias profiling and/or heuristic rules for data speculation, thus allowing existing frameworks to be extended to support both control and data speculation. Such a general framework is very useful for EPIC architectures that provide runtime checking (such as advanced load address table (ALAT)) on data speculation to guarantee the correctness of program execution. We use SSAPRE as one example to illustrate how to incorporate data speculation in partial redundancy elimination (PRE), register promotion, and strength reduction. Our extended framework allows both control and data speculation to be performed on top of SSAPRE and, thus, enables more aggressive speculative optimizations. The proposed framework has been implemented on Intel's Open Research Compiler (ORC). We present experimental data on some SPEC2000 benchmark programs to demonstrate the usefulness of this framework.

Dynamic register promotion of stack variables

“…In [14], speculative register promotion is performed with help from special hardware supports such as the ALAT (Advanced Load Address Table) to minimize the cost of address checking. However, ALAT is a specific feature in the Itanium processor family, not available in other architectures.…”

Optimal and Perfectly Parallel Algorithms for On-demand Data-Flow Analysis

“…On-demand analyses have several applications, such as (quoting from [35,47]) (i) narrowing down the focus to specific points of interest, (ii) narrowing down the focus to specific data-flow facts of interest, (iii) reducing work in preliminary phases, (iv) sidestepping incremental updating problems, and (v) offering demand analysis as a user-level operation. On-demand analysis is also extremely useful for speculative optimizations in just-in-time compilers [23,42,5,28], where dynamic information can dramatically increase the precision of the analysis. In this setting, it is crucial that the the on-demand analysis runs fast, to incur as little overhead as possible.…”

An Empirical Study of Data Speculation Use on the Intel Itanium 2 Processor

“…This work was later extended to multithreaded architectures [12]. Lin et al [15], [16], [17], use the Itanium's data speculations support to perform more general speculative optimizations. In particular, they extend Chow et al's extension of static single assignment form (called HSSA [18]) to incorporate speculative alias information obtained by alias profiling.…”