Performance Debugging and Tuning using an Instruction-Set Simulator

Instruction-set simulators allow programmers a detailed level of insight into, and control over, the execution of a program, including parallel programs and operating systems. In principle, instruction set simulation can model any target computer and gather any statistic. Furthermore, such simulators are usually portable, independent of compiler tools, and deterministic-allowing bugs to be recreated or measurements repeated. Though often viewed as being too slow for use as a general programming tool, in the last several years their performance has improved considerably. We describe SIMICS, an instruction set simulator of SPARC-based multiprocessors developed at SICS, in its rôle as a general programming tool. We discuss some of the benefits of using a tool such as SIMICS to support various tasks in software engineering, including debugging, testing, analysis, and performance tuning. We present in some detail two test cases, where we've used SimICS to support analysis and performance tuning of two applications, Penny and EQNTOTT. This work resulted in improved parallelism in, and understanding of, Penny, as well as a performance improvement for EQNTOTT of over a magnitude. We also present some early work on analyzing SPARC/Linux, demonstrating the ability of tools like SimICS to analyze operating systems

A semi-automatic parallelization tool for Java based on fork-join synchronization patterns

Because of the increasing availability of multi-core machines, clusters, Grids, and combinations of these environments, there is now plenty of computational power available for executing compute intensive applications. However, because of the overwhelming and rapid advances in distributed and parallel hardware and environments, today?s programmers are not fully prepared to exploit distribution and parallelism. In this sense, the Java language has helped in handling the heterogeneity of such environments, but there is a lack of facilities and tools to easily distributing and parallelizing applications. One solution to mitigate this problem and make some progress towards producing general tools seems to be the synthesis of semi-automatic parallelism and Parallelism as a Concern (PaaC), which allows parallelizing applications along with as little modifications on sequential codes as possible. In this paper, we discuss a new approach that aims at overcoming the drawbacks of current Java-based parallel and distributed development tools, which precisely exploit these new conceptsFil: Hirsch, Matias. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - CONICET - Tandil. Instituto Superior de Ingenieria del Software; Argentina;Fil: Zunino, Alejandro. Consejo Nacional de Invest.cientif.y Tecnicas. Ctro Cientifico Tecnologico Conicet - Tandil. Instituto Superior de Ingenieria del Software;Fil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - CONICET - Tandil. Instituto Superior de Ingenieria del Software

Scientific Computing Meets Big Data Technology: An Astronomy Use Case

Scientific analyses commonly compose multiple single-process programs into a dataflow. An end-to-end dataflow of single-process programs is known as a many-task application. Typically, tools from the HPC software stack are used to parallelize these analyses. In this work, we investigate an alternate approach that uses Apache Spark -- a modern big data platform -- to parallelize many-task applications. We present Kira, a flexible and distributed astronomy image processing toolkit using Apache Spark. We then use the Kira toolkit to implement a Source Extractor application for astronomy images, called Kira SE. With Kira SE as the use case, we study the programming flexibility, dataflow richness, scheduling capacity and performance of Apache Spark running on the EC2 cloud. By exploiting data locality, Kira SE achieves a 2.5x speedup over an equivalent C program when analyzing a 1TB dataset using 512 cores on the Amazon EC2 cloud. Furthermore, we show that by leveraging software originally designed for big data infrastructure, Kira SE achieves competitive performance to the C implementation running on the NERSC Edison supercomputer. Our experience with Kira indicates that emerging Big Data platforms such as Apache Spark are a performant alternative for many-task scientific applications

Neural Responding Machine for Short-Text Conversation

We propose Neural Responding Machine (NRM), a neural network-based response generator for Short-Text Conversation. NRM takes the general encoder-decoder framework: it formalizes the generation of response as a decoding process based on the latent representation of the input text, while both encoding and decoding are realized with recurrent neural networks (RNN). The NRM is trained with a large amount of one-round conversation data collected from a microblogging service. Empirical study shows that NRM can generate grammatically correct and content-wise appropriate responses to over 75% of the input text, outperforming state-of-the-arts in the same setting, including retrieval-based and SMT-based models.Comment: accepted as a full paper at ACL 201

A Survey on Compiler Autotuning using Machine Learning

Since the mid-1990s, researchers have been trying to use machine-learning based approaches to solve a number of different compiler optimization problems. These techniques primarily enhance the quality of the obtained results and, more importantly, make it feasible to tackle two main compiler optimization problems: optimization selection (choosing which optimizations to apply) and phase-ordering (choosing the order of applying optimizations). The compiler optimization space continues to grow due to the advancement of applications, increasing number of compiler optimizations, and new target architectures. Generic optimization passes in compilers cannot fully leverage newly introduced optimizations and, therefore, cannot keep up with the pace of increasing options. This survey summarizes and classifies the recent advances in using machine learning for the compiler optimization field, particularly on the two major problems of (1) selecting the best optimizations and (2) the phase-ordering of optimizations. The survey highlights the approaches taken so far, the obtained results, the fine-grain classification among different approaches and finally, the influential papers of the field.Comment: version 5.0 (updated on September 2018)- Preprint Version For our Accepted Journal @ ACM CSUR 2018 (42 pages) - This survey will be updated quarterly here (Send me your new published papers to be added in the subsequent version) History: Received November 2016; Revised August 2017; Revised February 2018; Accepted March 2018