On the Theory of Spatial and Temporal Locality

This paper studies the theory of caching and temporal and spatial locality. We show the following results: (1) hashing can be used to guarantee that caches with limited associativity behave as well as fully associative cache; (2) temporal locality cannot be characterized using one, or few parameters; (3) temporal locality and spatial locality cannot be studied separately; and (4) unlike temporal locality, spatial locality cannot be managed efficiently online

TaskInsight: Understanding Task Schedules Effects on Memory and Performance

Recent scheduling heuristics for task-based applications have managed to improve their by taking into account memory-related properties such as data locality and cache sharing. However, there is still a general lack of tools that can provide insights into why, and where, different schedulers improve memory behavior, and how this is related to the applications' performance. To address this, we present TaskInsight, a technique to characterize the memory behavior of different task schedulers through the analysis of data reuse between tasks. TaskInsight provides high-level, quantitative information that can be correlated with tasks' performance variation over time to understand data reuse through the caches due to scheduling choices. TaskInsight is useful to diagnose and identify which scheduling decisions affected performance, when were they taken, and why the performance changed, both in single and multi-threaded executions. We demonstrate how TaskInsight can diagnose examples where poor scheduling caused over 10% difference in performance for tasks of the same type, due to changes in the tasks' data reuse through the private and shared caches, in single and multi-threaded executions of the same application. This flexible insight is key for optimization in many contexts, including data locality, throughput, memory footprint or even energy efficiency.We thank the reviewers for their feedback. This work was supported by the Swedish Research Council, the Swedish Foundation for Strategic Research project FFL12-0051 and carried out within the Linnaeus Centre of Excellence UPMARC, Uppsala Programming for Multicore Architectures Research Center. This paper was also published with the support of the HiPEAC network that received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 687698.Peer ReviewedPostprint (published version

Intermediately Executed Code is the Key to Find Refactorings that Improve Temporal Data Locality

C

Performance analysis and optimization of automatic speech recognition

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Fast and accurate Automatic Speech Recognition (ASR) is emerging as a key application for mobile devices. Delivering ASR on such devices is challenging due to the compute-intensive nature of the problem and the power constraints of embedded systems. In this paper, we provide a performance and energy characterization of Pocketsphinx, a popular toolset for ASR that targets mobile devices. We identify the computation of the Gaussian Mixture Model (GMM) as the main bottleneck, consuming more than 80 percent of the execution time. The CPI stack analysis shows that branches and main memory accesses are the main performance limiting factors for GMM computation. We propose several software-level optimizations driven by the power/performance analysis. Unlike previous proposals that trade accuracy for performance by reducing the number of Gaussians evaluated, we maintain accuracy and improve performance by effectively using the underlying CPU microarchitecture. First, we use a refactored implementation of the innermost loop of the GMM evaluation code to ameliorate the impact of branches. Second, we exploit the vector unit available on most modern CPUs to boost GMM computation, introducing a novel memory layout for storing the means and variances of the Gaussians in order to maximize the effectiveness of vectorization. Third, we compute the Gaussians for multiple frames in parallel, so means and variances can be fetched once in the on-chip caches and reused across multiple frames, significantly reducing memory bandwidth usage. We evaluate our optimizations using both hardware counters on real CPUs and simulations. Our experimental results show that the proposed optimizations provide 2.68x speedup over the baseline Pocketsphinx decoder on a high-end Intel Skylake CPU, while achieving 61 percent energy savings. On a modern ARM Cortex-A57 mobile processor our techniques improve performance by 1.85x, while providing 59 percent energy savings without any loss in the accuracy of the ASR system.Peer ReviewedPostprint (author's final draft