By Ralf Karrenberg
Ralf Karrenberg offers Whole-Function Vectorization (WFV), an strategy that permits a compiler to instantly create code that exploits data-parallelism utilizing SIMD directions. Data-parallel purposes similar to particle simulations, inventory choice cost estimation or video deciphering require an identical computations to be played on large quantities of knowledge. 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 right now utilizing SIMD directions. the writer describes a complicated WFV set of rules that features a number of analyses and code iteration suggestions. He exhibits that this procedure improves the functionality of the generated code in numerous use cases.
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Additional resources for Automatic SIMD Vectorization of SSA-based Control Flow Graphs
A prominent example of such a program representation is LLVM bitcode [Lattner & Adve 2004] which we also use in our evaluation (Chapter 8). We will restrict ourselves to a subset of a language that contains only the relevant elements for this thesis. 1 shows its types and instructions. Other instructions, such as arithmetic and comparison operators are straightforward and omitted for the sake of brevity. This program representation reﬂects today’s consensus of instruction set architectures well.
A function is represented as a control ﬂow graph of instructions. , every variable has a single static assignment and every use of a variable is dominated by its deﬁnition. A prominent example of such a program representation is LLVM bitcode [Lattner & Adve 2004] which we also use in our evaluation (Chapter 8). We will restrict ourselves to a subset of a language that contains only the relevant elements for this thesis. 1 shows its types and instructions. Other instructions, such as arithmetic and comparison operators are straightforward and omitted for the sake of brevity.
The uniform property also depends on control ﬂow: If the condition that controls a conditional branch is varying, control ﬂow may diverge, and the program points where control ﬂow joins again are join points (blend). φ-functions at such join points and LCSSA φ-functions at exit points of divergent loops become varying: Consider a φ-function with constants as incoming values on all edges. , it is blend, the φ is not uniform but varying. This is because not all instances enter the φ’s program point from the same predecessor in every 42 5 SIMD Property Analyses execution, so the result has to be a vector of possibly diﬀerent constants for each instance.