Automatic SIMD Vectorization of SSA-based Control Flow by Ralf Karrenberg

By Ralf Karrenberg

Ralf Karrenberg provides Whole-Function Vectorization (WFV), an technique that enables a compiler to instantly create code that exploits data-parallelism utilizing SIMD directions. Data-parallel purposes equivalent to particle simulations, inventory alternative cost estimation or video deciphering require an analogous computations to be played on large quantities of information. with out WFV, one processor center executes a unmarried example of a data-parallel functionality. WFV transforms the functionality to execute a number of situations straight away utilizing SIMD directions. the writer describes a complicated WFV set of rules that features a number of analyses and code new release thoughts. He indicates that this process improves the functionality of the generated code in numerous use cases.

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2: Evaluation functions for the Operational Semantics. ω is an arithmetic or comparison operator, newptr returns a new memory location in μ, and Mg describes the memory effects of an execution of function g. A function has no effect if the program point’s mapping in @ (its predicate) is false. An unconditional branch b implicitly updates the predicate of its successor s with its own predicate: @(s) ∨ @(b). 1 Lifting to Vector Semantics Finally, in order to reason about SIMD programs, we lift OS to operate on vectors instead of scalar values.

Statements performing the same operations in the same order, inside a basic block, independent of loops. Such statements can be combined to SIMD instructions similar to instructions unrolled inside loops. Shin et al. [2005] extended the approach to also work in the presence of control flow by using predicates. Barik et al. [2010] introduced a similar approach based on dynamic programming. They exploit shuffle and horizontal vector operations and algebraic reassociation to uncover more potential for vectorization.

2012], Sierra [Leißa et al. 2014] and ispc [Pharr & Mark 2012] are languages that employ automatic SIMD vectorization specifically targeted at CPUs with SIMD instruction sets. Lately, the #pragma simd extension [Tian et al. 2012] is becoming more widespread in CPU compilers: it forces loop vectorization and can also vectorize functions. These vectorization approaches generally work in a similar fashion as WFV: they produce SIMD code from scalar source code in a data-parallel way. For simple cases, all these languages will produce code similar to WholeFunction Vectorization.

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