By Ralf Karrenberg
Ralf Karrenberg provides Whole-Function Vectorization (WFV), an strategy that permits a compiler to instantly create code that exploits data-parallelism utilizing SIMD directions. Data-parallel purposes comparable to particle simulations, inventory alternative expense estimation or video deciphering require a similar computations to be played on large quantities of information. with no WFV, one processor middle executes a unmarried example of a data-parallel functionality. WFV transforms the functionality to execute a number of cases immediately utilizing SIMD directions. the writer describes a complicated WFV set of rules that features a number of analyses and code new release concepts. He indicates that this process improves the functionality of the generated code in a number of use cases.
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Additional resources for Automatic SIMD Vectorization of SSA-based Control Flow Graphs
This includes arbitrary control ﬂow, nested data structures, operations with side eﬀects, and operations without vector equivalent. In general, the algorithm is able to transform any function, but possibly with signiﬁcant overhead. Arbitrary Control Flow. The control-ﬂow to data-ﬂow conversion that will be presented in the following sections can convert any kind of control ﬂow, including loops with multiple exits, nested loops, and exits that leave multiple nesting levels. 5 also presents an extension for irreducible control ﬂow.
3. 2). These operations blend together values from disjoint paths that were executed by diﬀerent instances. 4. 3). This means that, in such regions, all branches except for loop back edges are removed and code of originally disjoint paths is merged into one path. 5. 4). R. 2 Algorithmic Challenges The WFV algorithm is designed to vectorize code for a large class of language features. This includes arbitrary control ﬂow, nested data structures, operations with side eﬀects, and operations without vector equivalent.
1). 1 Late broadcasting may require less operations. The function purefn is assumed to not produce any side eﬀects, otherwise it would be split in both cases. ; scalar code . a0 / a1 / a2 are uniform , v is varying % x = fadd float % a0 , % a1 % y = call float @purefn ( float %x , float % a2 ) % z = fsub float %y , % v ; vector code ( eager b r o a d c a s t ) % v0 = broadcast float % a0 to <4 x float > % v1 = broadcast float % a1 to <4 x float > % v2 = broadcast float % a2 to <4 x float > % vx = fadd <4 x float > % v0 , % v1 % vx0 = extractelement <4 x float > % vx , i32 0 % vy0 = call float @purefn ( float % vx0 , % a2 ) % vy = broadcast float % vy0 to <4 x float > % vz = fsub <4 x float > % vy , % v ; vector code ( late b r o a d c a s t ) % x = fadd float % a0 , % a1 % y = call float @purefn ( float %x , float % a2 ) % vy = broadcast float % y to <4 x float > % z = fsub <4 x float > % vy , % v Non-Divergent Control Flow.
Automatic SIMD Vectorization of SSA-based Control Flow Graphs by Ralf Karrenberg