SLP Auto-vectorization converts straight-line code into vector code. It scans input code for groups of instructions that can be combined into vectors and replaces them with their corresponding vector instructions. This work introduces Super-Node SLP (SN-SLP), a new SLP-style algorithm, optimized for expressions that include a commutative operator (such as addition) and its corresponding inverse element (subtraction). SN-SLP uses the algebraic properties of commutative operators and their inverse elements to enable additional transformations that extend auto-vectorization to cases difficult for state-of-the-art auto-vectorizing compilers. We implemented SN-SLP in LLVM. Our evaluation on a real system demonstrates considerable performance improvements of benchmark code with no significant change in compilation time.

In this paper, we evaluate the emerging Transactional Memory (TM) area by developing a set of Java transactional memory workloads and studying their performance under a Java Software Transactional Memory (STM) system and comparing them to their lock based counterparts. We provide a detailed performance and memory consumption analysis of the overheads of software transactional memory and transactional workloads within a production quality open source Java Runtime system. Additionally, we detail the impact of the various performance optimizations in both workloads and the underlying runtime system to improving both single thread performance and scalability.