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parallel binary code analysis 21

Parallel binary code analysis

引用

26th ACM SIGPLAN symposium on principles and practice of parallel programming, PPoPP 2021

作者： Meng, Xiaozhu Anderson, Jonathon M. Mellor-Crummey, John Krentel, Mark W. Miller, Barton P. Milaković, Sran Department of Computer Science Rice University Houston TX United States Computer Sciences Department University of Wisconsin-Madison MadisonWI United States

ISBN: (纸本)9781450382946

Binary code analysis is widely used to help assess a program's correctness, performance, and provenance. Binary analysis applications often construct control flow graphs, analyze data flow, and use debugging information to understand how machine code relates to source lines, inlined functions, and data types. To date, binary analysis has been single-threaded, which is too slow for convenient use in performance tuning workflows where it is used to help attribute performance to complex applications with large binaries. this paper describes our design and implementation for accelerating the task of constructing control flow graphs (CFGs) from binaries by using multithreading. Prior research focuses on algorithms for analysis of challenging code constructs encountered while constructing CFGs, including functions sharing code, jump tables, non-returning functions, and tail calls. these algorithms are described from a program analysis perspective and are not suitable for direct parallel implementation. We abstract the task of constructing CFGs as repeated applications of several core CFG operations that include creating functions, basic blocks, and edges. We then derive CFG operation dependency, commutativity, and monotonicity. these operation properties guide our design of a new parallel analysis for constructing CFGs. Using 64 threads, we achieved as much as 25× speedup for constructing CFGs and 8× for a performance analysis tool that leverages our new analysis to recover program structure. © 2021 ACM.

关键词： Graphic methods

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parallel algorithms for image histogramming and connected components with an experimental study

Parallel algorithms for image histogramming and connected co...

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proceedings of the 5th ACM SIGPLAN symposium on principles and practice of parallel programming

作者： Bader, David A. JaJa, Joseph Univ of Maryland College Park United States

this paper presents efficient and portable implementations of two useful primitives in image processing algorithms, histogramming and connected components. Our general framework is a single-address space, distributed memory programming model. We use efficient techniques for distributing and coalescing data as well as efficient combinations of task and data parallelism. Our connected components algorithm uses a novel approach for parallel merging which performs drastically limited updating during iterative steps, and concludes with a total consistency update at the final step. the algorithms have been coded in Split-C and run on a variety of platforms. Our experimental results are consistent with the theoretical analysis and provide the best known execution times for these two primitives, even when compared with machine-specific implementations. More efficient implementations of Split-C will likely result in even faster execution times.

关键词： parallel algorithms

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Interruptible Tasks: Treating Memory Pressure As Interrupts for Highly Scalable Data-parallel Programs 15

Interruptible Tasks: Treating Memory Pressure As Interrupts ...

引用

25th ACM symposium on Operating Systems principles (SOSP)

作者： Fang, Lu Khanh Nguyen Xu, Guoqing Demsky, Brian Lu, Shan Univ Calif Irvine Irvine CA 92717 USA Univ Chicago Chicago IL 60637 USA

ISBN: (纸本)9781450338349

Real-world data-parallel programs commonly suffer from great memory pressure, especially when they are executed to process large datasets. Memory problems lead to excessive GC effort and out-of-memory errors, significantly hurting system performance and scalability. this paper proposes a systematic approach that can help data-parallel tasks survive memory pressure, improving their performance and scalability without needing any manual effort to tune system parameters. Our approach advocates interruptible task (ITask), a new type of data-parallel tasks that can be interrupted upon memory pressure-with part or all of their used memory reclaimed-and resumed when the pressure goes away. To support ITasks, we propose a novel programming model and a runtime system, and have instantiated them on two state-of-the-art platforms Hadoop and Hyracks. A thorough evaluation demonstrates the effectiveness of ITask: it has helped real-world Hadoop programs survive 13 out-of-memory problems reported on StackOverflow;a second set of experiments with 5 already well-tuned programs in Hyracks on datasets of different sizes shows that the ITask-based versions are 1.5-3x faster and scale to 3-24x larger datasets than their regular counterparts.

关键词： Scalability

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Unique fixed point induction for McCarthy’s amb 24th

引用

24th International symposium on Mathematical Foundations of Computer Science, MFCS 1999

作者： Lassen, Søren B. Moran, Andrew University of Cambridge Computing Laboratory Pembroke Street CambridgeCB2 3QG United Kingdom Department of Computing Science Chalmers University of Technology Göteborg412 96 Sweden

ISBN: (纸本)3540664084

We develop an operational theory of higher-order functions, recursion, and fair non-determinism for a non-trivial, higher-order, call-by-name functional programming language extended with McCarthy’s amb. Implemented via fair parallel evaluation, functional programming with amb is very expressive. However, conventional semantic fixed point principles for reasoning about recursion fail in the presence of fairness. Instead, we adapt higher-order operational methods to deal with fair non-determinism. We present two natural semantics, describing may-and must-convergence, and define a notion of contextual equivalence over these two modalities. the presence of amb raises special difficulties when reasoning about contextual equivalence. In particular, we report on a challenging open problem with regard to the validity of bisimulation proof methods. We develop two sound and useful reasoning methods which, in combination, enable us to prove a rich collection of laws for contextual equivalence and also provide a unique fixed point induction rule, the first proof rule for reasoning about recursion in the presence of fair non-determinism. © Springer-Verlag Berlin Heidelberg 1999.

关键词： Functional programming

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Optimizing Data Popularity Conscious Bloom Filters

Optimizing Data Popularity Conscious Bloom Filters

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27th Annual ACM symposium on principles of Distributed Computing

作者： Zhong, Ming Lu, Pin Shen, Kai Seiferas, Joel Google Inc. Department of Computer Science University of Rochester University of Rochester

ISBN: (纸本)9781595939890

Bloom filters are compact set, representations that support set membership queries with small, one-sided error probabilities. Standard Bloom filters are oblivious to object popularity ill sets, and membership queries. However, sets and queries in many distributed applications follow known, stable, highly skewed distributions (e.g., Zipf-like). this paper studies the problem of minimizing the, false-positive probability of a Bloom filter by adapting the number of hashes used for each data object to its popularity in sets and membership queries. We model the problem as a constrained nonlinear integer program and propose two polynomial-time solutions with bounded approximation ratios - one is a 2-approximation algorithm with O(N-c) running time (c >= 6 in practice): the other is a (2 + epsilon)-approximation algorithm with running time O(N-2/epsilon), epsilon > 0. Here N denotes the total number of distinct data objects that appear in set's or queries. We quantitatively evaluate our proposed approach oil two distributed applications (cooperative caching and full-text keyword searching) driven by real-life data traces. Compared to standard Bloom filters, our data popularity-conscious Bloom filters achieve up to 24 and 27 times false-positive probability reduction for the two applications respectively. the quantitative evaluation also validates our solution's bounded approximation ratio to the optimal.

关键词： Bloom filters optimization dynamic programming knapsack problems

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Training one DeePMD Model in Minutes: a Step towards Online Learning 24

Training one DeePMD Model in Minutes: a Step towards Online ...

引用

29th ACM SIGPLAN Annual symposium on principles and practice of parallel programming (PPoPP)

作者： Hu, Siyu Zhao, Tong Sha, Qiuchen Li, Enji Meng, Xiangyu Liu, Liping Wang, Lin-Wang Tan, Guangming Jia, Weile Chinese Acad Sci Inst Comp Technol State Key Lab Proc Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China China Univ Petr Qingdao Inst Software Coll Comp Sci & Technol Qingdao Peoples R China Chinese Acad Sci Inst Semicond Beijing Peoples R China

ISBN: (纸本)9798400704352

Neural Network Molecular Dynamics (NNMD) has become a major approach in material simulations, which can speedup the molecular dynamics (MD) simulation for thousands of times, while maintaining ab initio accuracy, thus has a potential to fundamentally change the paradigm of material simulations. However, there are two time-consuming bottlenecks of the NNMD developments. One is the data access of ab initio calculation results. the other, which is the focus of the current work, is reducing the training time of NNMD model. the training of NNMD model is different from most other neural network training because the atomic force (which is related to the gradient of the network) is an important physical property to be fit. Tests show the traditional stochastic gradient methods, like the Adam algorithms, cannot efficiently deploy the multisample minibatch algorithm. As a result, a typical training (taking the Deep Potential Molecular Dynamics (DeePMD) as an example) can take many hours. In this work, we designed a heuristic minibatch quasi-Newtonian optimizer based on Extended Kalman Filter method. An early reduction of gradient and error is adopted to reduce memory footprint and communication. the memory footprint, communication and settings of hyper-parameters of this new method are analyzed in detail. Computational innovations such as customized kernels of the symmetry-preserving descriptor are applied to exploit the computing power of the heterogeneous architecture. Experiments are performed on 8 different datasets representing different real case situations, and numerical results show that our new method has an average speedup of 32.2 compared to the Reorganized Layer-wised Extended Kalman Filter with 1 GPU, reducing the absolute training time of one DeePMD model from hours to several minutes, making it one step toward online training.

关键词： parallel training Molecular dynamics First principle ab initio GPU

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NUMA-aware graph-structured analytics 2015

NUMA-aware graph-structured analytics

引用

20th ACM SIGPLAN symposium on principles and practice of parallel programming, PPoPP 2015

作者： Zhang, Kaiyuan Chen, Rong Chen, Haibo Shanghai Key Laboratory of Scalable Computing and Systems Institute of Parallel and Distributed Systems Shanghai Jiao Tong University China

ISBN: (纸本)9781450332057

Graph-structured analytics has been widely adopted in a number of big data applications such as social computation, web-search and recommendation systems. though much prior research focuses on scaling graph-analytics on distributed environments, the strong desire on performance per core, dollar and joule has generated considerable interests of processing large-scale graphs on a single server-class machine, which may have several terabytes of RAM and 80 or more cores. However, prior graph-analytics systems are largely neutral to NUMA characteristics and thus have suboptimal performance. this paper presents a detailed study of NUMA characteristics and their impact on the efficiency of graph-analytics. Our study uncovers two insights: 1) either random or interleaved allocation of graph data will significantly hamper data locality and parallelism;2) sequential inter-node (i.e., remote) memory accesses have much higher bandwidth than both intra- and inter-node random ones. Based on them, this paper describes Polymer, a NUMA-aware graph-analytics system on multicore with two key design decisions. First, Polymer differentially allocates and places topology data, application-defined data and mutable runtime states of a graph system according to their access patterns to minimize remote accesses. Second, for some remaining random accesses, Polymer carefully converts random remote accesses into sequential remote accesses, by using lightweight replication of vertices across NUMA nodes. To improve load balance and vertex convergence, Polymer is further built with a hierarchical barrier to boost parallelism and locality, an edge-oriented balanced partitioning for skewed graphs, and adaptive data structures according to the proportion of active vertices. A detailed evaluation on an 80-core machine shows that Polymer often outperforms the state-of-the-art single-machine graph-analytics systems, including Ligra, X-Stream and Galois, for a set of popular real-world and synthetic grap

关键词： Random access storage

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A Predicate Construct for Declarative programming in Imperative Languages 22

A Predicate Construct for Declarative Programming in Imperat...

引用

24th International symposium on principles and practice of Declarative programming, PPDP 2022

作者： Smith, James Guo, Xiangyu Bansal, Ajay Arizona State University MesaAZ United States

ISBN: (纸本)9781450397032

Imperative and object-oriented programming languages are among the most common languages for general-purpose programming. these languages work well for handling many common tasks necessary for most applications. However, there are still many hard problems that remain difficult to implement directly in imperative languages. Declarative languages have worked well for solving many of these problems by providing a syntax that allows the user to focus on modeling the problem rather than on designing an algorithm. Logic programming languages, like Prolog, have seen success in constraint satisfaction problems, logical databases, and various NP-Hard problems. Unfortunately, these languages have not seen the same success in general-purpose programming, and most of the problems they solve do not exist in isolation. Furthermore, many imperative programmers are still unfamiliar with and unaware of logic programming. In this work, we aim to integrate a logical predicate, borrowed from logic programming, into an imperative language to facilitate a model-based approach to programming. Rather than attempting to embed Prolog directly into the language, we provide a logic for reasoning over imperative expressions using the common Boolean operators familiar to imperative programmers. While logic programming relies on unification, our predicate relies on variable assignment which is standard in imperative languages. We have implemented the predicate into the syntax of the Python programming language along with a solver built into the runtime for solving queries on the predicate. We demonstrate the use of the construct through solving commonly-known problems, such as graph coloring and the N-Queens problem. To evaluate the viability of our predicate, we compare the performance results of our predicate against current implementations of Prolog. By bringing this predicate construct to imperative languages, it is our hope that we can help to bridge the gap between imperative and logic prog

关键词： Computer circuits

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Java for high performance computing: Assessment of current research and practice 09

Java for high performance computing: Assessment of current r...

引用

7th International Conference on principles and practice of programming in Java, PPPJ 2009

作者： Taboada, Guillermo L. Touriño, Juan Doallo, Ramón Computer Architecture Group University of a Coruña A Coruña Spain

ISBN: (纸本)9781605585987

the rising interest in Java for High Performance Computing (HPC) is based on the appealing features of this language for programming multi-core cluster architectures, particularly the built-in networking and multithreading support, and the continuous increase in Java Virtual Machine (JVM) performance. However, its adoption in this area is being delayed by the lack of analysis of the existing programming options in Java for HPC and evaluations of their performance, as well as the unawareness of the current research projects in this field, whose solutions are needed in order to boost the embracement of Java in HPC. this paper analyzes the current state of Java for HPC, both for shared and distributed memory programming, presents related research projects, and finally, evaluates the performance of current Java HPC solutions and research developments on a multi-core cluster with a high-speed network, InfiniBand, and a 24-core shared memory machine. the main conclusions are that: (1) the significant interest on Java for HPC has led to the development of numerous projects, although usually quite modest, which may have prevented a higher development of Java in this field;and (2) Java can achieve almost similar performance to native languages, both for sequential and parallel applications, being an alternative for HPC programming. thus, the good prospects of Java in this area are attracting the attention of both industry and academia, which can take significant advantage of Java adoption in HPC. Copyright 2009 ACM.

关键词： Message passing

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30th European symposium on programming, ESOP 2021 Held as Part of the European Joint Conferences on theory and practice of Software, ETAPS 2021

30th European Symposium on Programming, ESOP 2021 Held as Pa...

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30th European symposium on programming, ESOP 2021 Held as Part of the European Joint Conferences on theory and practice of Software, ETAPS 2021

ISBN: (纸本)9783030720186

the proceedings contain 24 papers. the special focus in this conference is on programming. the topics include: Sound and Complete Concolic Testing for Higher-order Functions;strong-Separation Logic;types for Complexity of parallel Computation in Pi-Calculus;checking Robustness Between Weak Transactional Consistency Models;verified Software Units;an Automated Deductive Verification Framework for Circuit-building Quantum Programs;nested Session Types;coupled Relational Symbolic Execution for Differential Privacy;graded Hoare Logic and its Categorical Semantics;coupled relational symbolic execution for differential privacy;graded hoare logic and its categorical semantics;etaps foreword;preface;For a Few Dollars More: Verified Fine-Grained Algorithm Analysis Down to LLVM;run-time Complexity Bounds Using Squeezers;complete trace models of state and control;session Coalgebras: A Coalgebraic View on Session Types and Communication Protocols;correctness of Sequential Monte Carlo Inference for Probabilistic programming Languages;densities of Almost Surely Terminating Probabilistic Programs are Differentiable Almost Everywhere;graded Modal Dependent Type theory;automated Termination Analysis of Polynomial Probabilistic Programs;bayesian strategies: probabilistic programs as generalised graphical models;data Flow Analysis of Asynchronous Systems using Infinite Abstract Domains.

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