No abstract

With speculative parallelization, code sections that cannot be fully analyzed by the compiler are optimistically executed in parallel. Hardware schemes are fast but expensive and require modifications to the processors and/or memory system. Software schemes require no changes to the hardware of existing shared-memory systems, but can suffer from significant overheads involved with the speculative execution. In fact, the performance of software schemes is highly dependent on application characteristics, the design and implementation of the scheme, and the system configuration and size. This paper explores the design space of a recently proposed software speculative parallelization scheme. In the process, we gain insight into the most beneficial features of software schemes for speculative parallelization, as well as the most influential application characteristics. For instance, experimental results show that, contrary to intuition, checking for data dependence violations on every speculative store, as opposed to at commit time, leads to little performance degradation in the worst case and to significantly better performance with large configurations. Also, scheduling policies based on windows can perform very close to fully dynamic policies with a fraction of the memory overhead. Finally, experimental results show consistent speedups in the execution of loops that cannot be parallelized at compile time, both with and without RAW data dependences, for 4 to 32 processors.

With speculative parallelization, code sections that cannot be fully analyzed by the compiler are aggressively executed in parallel. Hardware schemes are fast but expensive and require modifications to the processors and memory system. Software schemes require no extra hardware but can be inefficient.This paper proposes a new software-only speculative parallelization scheme. The scheme is developed after a systematic evaluation of the design options available and is shown to be efficient and robust and to outperform previously proposed schemes. The novelty and performance advantage of the scheme stem from the use of carefully tuned data structures, synchronization policies, and scheduling mechanisms. Experimental results show that our scheme has small overheads and, for applications with few or no data dependence violations, realizes on average 71% of the speedup of a manually parallelized version of the code, outperforming two recently proposed software-only speculative parallelization schemes. For applications with many data dependence violations, our performance monitors and switches can effectively curb the performance degradation.

Abstract-Automatic data distribution is a key feature to obtain efficient implementations from abstract and portable parallel codes. We present a highly efficient and extensible runtime library that integrates techniques for automatic data partition and mapping. It uses a novel approach to define an abstract interface and a plug-in system to encapsulate different types of regular and irregular techniques, helping to generate codes which are independent of the exact mapping functions selected. Currently, it supports hierarchical tiling of arrays with dense and stride domains, that allows the implementation of both data and task parallelism using a SPMD model. It automatically computes appropriate domain partitions for a selected virtual topology, mapping them to available processors with static or dynamic load-balancing techniques. Our library also allows the construction of reusable communication patterns that efficiently exploit MPI communication capabilities. The use of our library greatly reduces the complexity of data distribution and communication, hiding the details of the underlying architecture. The library can be used as an abstract layer for building generic tiling operations as well. Our experimental results show that the use of this library allows to achieve similar performance as carefully-implemented manual versions for several, well-known parallel kernels and benchmarks in distributed and multicore systems, and substantially reduces programming effort.

The Single-Source Shortest Path (SSSP) problem arises in many different fields. In this paper we present a GPUbased version of the Crauser et al. SSSP algorithm. Our work significantly speeds up the computation of the SSSP, not only with respect to the CPU-based version, but also to other state-ofthe-art GPU implementation based on Dijkstra, due to Martín et al. Both GPU implementations have been evaluated using the last Nvidia architecture (Kepler). Our experimental results show that the new GPU-Crauser algorithm leads to speed-ups from 13× to 220× with respect to the CPU version and a performance gain of up to 17% with respect the GPU-Martín algorithm.