Name
Abstract | ||
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Achieving high performance for compute bounded numerical kernels typically requires an expert to hand select an appropriate set of Single-instruction multiple-data (SIMD) instructions, then statically scheduling them in order to hide their latency while avoiding register spilling in the process. Unfortunately, this level of control over the code forces the expert to trade programming abstraction for performance which is why many performance critical kernels are written in assembly language. An alternative is to either resort to auto-vectorization (see Figure 1) or to use intrinsic functions, both features offered by compilers. However, in both scenarios the expert loses control over which instructions are selected, which optimizations are applied to the code and moreover how the instructions are scheduled for a target architecture. Ideally, the expert would need assembly-like control over their SIMD instructions beyond what intrinsics provide while maintaining a C-level abstraction for the non-performance critical parts.
In this paper, we bridge the gap between performance and abstraction for SIMD instructions through the use of custom macro intrinsics that provide the programmer control over the instruction selection, and scheduling, while leveraging the compiler to manage the registers. This provides the best of both assembly and vector intrinsics programming so that a programmer can obtain high performance implementations within the C programming language.
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Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2870650.2870654 | WPMVP '16: Workshop on Programming Models for SIMD/Vector Programming 2016
Barcelona
Spain
March, 2016 |
Keywords | Field | DocType |
SIMD, Performance Engineering | Programmer,Programming language,Computer science,Instruction selection,Parallel computing,SIMD,Assembly language,Compiler,Macro,Intrinsics,Operating system,Intrinsic function | Conference |
ISBN | Citations | PageRank |
978-1-4503-4060-1 | 2 | 0.38 |
References | Authors | |
6 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Richard Veras | 1 | 280 | 9.85 |
| Popovici, Doru Thom | 2 | 30 | 5.61 |
| Tze Meng Low | 3 | 146 | 19.62 |
| Franz Franchetti | 4 | 974 | 88.39 |