Refactoring Loops with Nested IFs for SIMD Extensions Without Masked Instructions

Most CPUs in heterogeneous systems are now equipped with SIMD (Single Instruction Multiple Data) extensions that operate on short vectors in parallel to enable high performance. Refactoring programs for such systems relies on vectorization, i.e., transforming into a form with SIMD-instructions. We improve the state of the art in refactoring loops with nested IF-statements that are notoriously difficult to vectorize. For IF-statements whose conditions are independent of the loop variable, we improve the classical loop unswitching method, such that it can tackle nested IFs. For IF-statements whose conditions change with loop iterations, we develop a novel IF-select transformation method: (1) it can work with arbitrarily nested IFs, and (2) while previous methods rely on either masked instructions or hardware support for predicated execution, our method works for SIMD extensions without such operations (as found, e.g., in IBM Power8 and ARM Cortex-A8). Our experimental evaluation for the SPEC CPU2006 benchmark suite is conducted on an SW26010 processor used in the Sunway TaihuLight supercomputer (#2 in the TOP500 list); it demonstrates the performance advantages of our implemented approach over the vectorizer of the Open64 compiler.