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Protecting Synchronization Mechanisms of parallel Big Data Kernels via Logging

IEEE TRANSACTIONS ON COMPUTERS 2022年第9期71卷 2156-2162页

作者： LeCompte, Travis Peng, Lu Yuan, Xu Tzeng, Nian-Feng Louisiana State Univ Baton Rouge LA 70803 USA Univ Louisiana Lafayette Lafayette LA 70504 USA

With the growing effort to reduce power consumption in machines, fault tolerance becomes more of a concern. This holds particularly for large-scale computing, where execution failures due to soft faults waste excessive time and resources. These large-scale applications are normally parallel in nature and rely on control structures tailored specifically for parallel computing, such as locks and barriers. While there are many studies on resilient software, to our knowledge none of them focus on protecting these parallel control structures. In this work, we present a method of ensuring the correct operation of both locks and barriers in parallel applications. Our method tracks the memory locations used within parallel sections and detects a violation of the control structures. Upon detecting any violation, the violating thread is rolled back to the beginning of the structure and reattempts it, similar to rollback mechanisms in transactional memory systems. We test the method on representative samples of the BigDataBench kernels and find it exhibits a mean error reduction of 93.6% for basic mutex locks and barriers with a mean 6.55% execution time overhead at 64 threads. Additionally, we provide a comparison to transactional memory methods and demonstrate up to a mean 57.5% execution time overhead reduction.

关键词： Instruction sets Synchronization Transient analysis Checkpointing Kernel Fault tolerant systems Fault tolerance Algorithmic resilience barriers fault tolerance locks parallel programming

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What Refactoring Topics Do Developers Discuss? A Large Scale Empirical Study Using Stack Overflow

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IEEE ACCESS 2022年 10卷 56362-56374页

作者： Abid, Chaima Gaaloul, Khouloud Kessentini, Marouane Alizadeh, Vahid Univ Michigan Dept Comp & Informat Sci Dearborn MI 48128 USA Oakland Univ Sch Engn & Comp Sci Rochester MI 48309 USA DePaul Univ Coll Comp & Digital Media Chicago IL 60614 USA

Due to the growing complexity of software systems, there has been a dramatic increase in research and industry demand on refactoring. Refactoring research nowadays addresses challenges beyond code transformation to include, but not limited to, scheduling the opportune time to carry refactoring, recommending specific refactoring activities, detecting refactoring opportunities and testing the correctness of applied refactorings. Very few studies focused on the challenges that practitioners face when refactoring software systems and what should be the current refactoring research focus from the developers' perspective. Without such knowledge, tool builders invest in the wrong direction, and researchers miss many opportunities for improving the practice of refactoring. In this paper, we collected data from the popular online Q&A site, Stack Overflow, and analyzed posts to identify what do developers ask about refactoring. We clustered these questions to find the different refactoring related topics using one of the most popular topic modeling algorithms, Latent Dirichlet Allocation (LDA). We found that developers are asking about design patterns, design and user interface refactoring, web services, parallel programming, and mobile apps. We also identified what popular refactoring challenges are the most difficult and the current important topics and questions related to refactoring. Moreover, we discovered gaps between existing research on refactoring and the challenges developers face. To the best of our knowledge, this paper represents the first Stack Overflow study to identify the refactoring topics discussed by developers. Our study can help researchers to focus on practical refactoring problems, practitioners know more about current challenges and build better refactoring tools, and educators revise curriculum to target current needs on refactoring.

关键词： Faces Software systems Resource management parallel programming Mobile applications Market research Information services Empirical study stack overflow refactoring

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Execution Time Estimation of Multithreaded Programs With Critical Sections

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IEEE TRANSACTIONS ON parallel AND DISTRIBUTED SYSTEMS 2022年第10期33卷 2470-2481页

作者： Kagaris, Dimitri Dutta, Sourav Eyerman, Stijn Southern Illinois Univ Sch Elect Comp & Biomed Engn Carbondale IL 62901 USA Ramapo Coll Sch Theoret & Appl Sci Mahwah NJ 07430 USA Intel Corp B-2550 Kontich Belgium

The ideal benefit of parallelizing/multithreading a program is diminished in practice by several factors such as hardware scaling, memory bandwidth, power constraints, and synchronization due to critical sections. Several models have been proposed in the past to estimate the resulting performance and extend the traditional Amdahl's law. In this work, we focus on the effect of synchronization, and develop a model for the execution time estimation of multithreaded programs under the presence of critical sections. The proposed model is applicable to multiple different critical sections and generalizes and improves previously proposed models. Experimental results on simulated, synthetic and benchmark examples show that the proposed model provides accurate approximations.

关键词： parallel programming performance estimation critical sections mutual exclusion synchronization concurrency modelling

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Errors Classification and Static Detection Techniques for Dual-programming Model (OpenMP and OpenACC)

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IEEE ACCESS 2022年 10卷 117808-117826页

作者： Basloom, Huda Saleh Dahab, Mohamed Yehia Alghamdi, Ahmed Mohammed Eassa, Fathy Elbouraey Al-Ghamdi, Abdullah Saad Al-Malaise Haridi, Seif King Abdulaziz Univ Fac Comp & Informat Technol Dept Comp Sci Jeddah 21514 Saudi Arabia Agr Res Ctr ARC Giza 12619 Egypt Univ Jeddah Coll Comp Sci & Engn Dept Software Engn Jeddah 21493 Saudi Arabia King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Syst Jeddah 21589 Saudi Arabia KTH Royal Inst Technol Dept Comp Sci S-11428 Stockholm Sweden

Recently, incorporating more than one programming model into a system designed for high performance computing (HPC) has become a popular solution to implementing parallel systems. Since traditional programming languages, such as C, C++, and Fortran, do not support parallelism at the level of multi-core processors and accelerators, many programmers add one or more programming models to achieve parallelism and accelerate computation efficiently. These models include Open Accelerators (OpenACC) and Open Multi-Processing (OpenMP), which have recently been used with various models, including Message Passing Interface (MPI) and Compute Unified Device Architecture (CUDA). Due to the difficulty of predicting the behavior of threads, runtime errors cannot be predicted. The compiler cannot identify runtime errors such as data races, race conditions, deadlocks, or livelocks. Many studies have been conducted on the development of testing tools to detect runtime errors when using programming models, such as the combinations of OpenACC with MPI models and OpenMP with MPI. Although more applications use OpenACC and OpenMP together, no testing tools have been developed to test these applications to date. This paper presents a testing tool for detecting runtime using a static testing technique. This tool can detect actual and potential runtime errors during the integration of the OpenACC and OpenMP models into systems developed in C++. This tool implement error dependency graphs, which are proposed in this paper. Additionally, a dependency graph of the errors is provided, along with a classification of runtime errors that result from combining the two programming models mentioned earlier.

关键词： parallel programming high-performance computing OpenMP OpenACC deadlock runtime errors race condition static testing software testing testing tools classifications exascale systems programming models

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Analysing software prefetching opportunities in hardware transactional memory

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JOURNAL OF SUPERCOMPUTING 2022年第1期78卷 919-944页

作者： Shimchenko, Marina Titos-Gil, Ruben Fernandez-Pascual, Ricardo Acacio, Manuel E. Kaxiras, Stefanos Ros, Alberto Jimborean, Alexandra Uppsala Univ Dept Comp Syst Uppsala Sweden Univ Murcia Comp Engn Dept Murcia Spain

Hardware transactional memory emerged to make parallel programming more accessible. However, the performance pitfall of this technique is squashing speculatively executed instructions and re-executing them in case of aborts, ultimately resorting to serialization in case of repeated conflicts. A significant fraction of aborts occurs due to conflicts (concurrent reads and writes to the same memory location performed by different threads). Our proposal aims to reduce conflict aborts by reducing the window of time during which transactional regions can suffer conflicts. We achieve this by using software prefetching instructions inserted automatically at compile-time. Through these prefetch instructions, we intend to bring the necessary data for each transaction from the main memory to the cache before the transaction itself starts to execute, thus converting the otherwise long latency cache misses into hits during the execution of the transaction. The obtained results show that our approach decreases the number of aborts by 30% on average and improves performance by up to 19% and 10% for two out of the eight evaluated benchmarks. We provide insights into when our technique is beneficial given certain characteristics of the transactional regions, the advantages and disadvantages of our approach, and finally, discuss potential solutions to overcome some of its limitations.

关键词： Hardware transactional memory parallel programming Compiler Software prefetching

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Optimizing the Cray Graph Engine for performant analytics on cluster, SuperDome Flex, Shasta systems and cloud deployment

Optimizing the Cray Graph Engine for performant analytics on...

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作者： Rickett, Christopher D. Maschhoff, Kristyn J. Sukumar, Sreenivas R. Hewlett Packard Enterprise United States

We present updates to the Cray Graph Engine, a high performance in-memory semantic graph database, which enable performant execution across multiple architectures as well as deployment in a container to support cloud and as-a-service graph analytics. This paper discusses the changes required to port and optimize CGE to target multiple architectures, including Cray Shasta systems, large shared-memory machines such as SuperDome Flex (SDF), and cluster environments such as Apollo systems. The porting effort focused primarily on removing dependences on XPMEM and Cray PGAS and replacing these with a simplified PGAS library based upon POSIX shared memory and one-sided MPI, while preserving the existing Coarray-C++ CGE code base. We also discuss the containerization of CGE using Singularity and the techniques required to enable container performance matching native execution. We present early benchmarking results for running CGE on the SDF, Infiniband clusters and Slingshot interconnect-based Shasta systems. © 2024 John Wiley & Sons Ltd.

关键词： parallel programming

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Optimal Path Planning of Autonomous Marine Vehicles in Stochastic Dynamic Ocean Flows Using a GPU-Accelerated Algorithm

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IEEE JOURNAL OF OCEANIC ENGINEERING 2022年第4期47卷 864-879页

作者： Chowdhury, Rohit Subramani, Deepak Indian Inst Sci Dept Computat & Data Sci Bengaluru 560012 India

Autonomous marine vehicles play an essential role in many ocean science and engineering applications. Planning time and energy optimal paths for these vehicles to navigate in stochastic dynamic ocean environments are essential to reduce operational costs. In some missions, they must also harvest solar, wind or wave energy (modeled as a stochastic scalar field) and move in optimal paths that minimize net energy consumption. Markov decision processes (MDPs) provide a natural framework for sequential decision making for robotic agents in such environments. However, building a realistic model and solving the modeled MDP becomes computationally expensive in large-scale real-time applications, warranting the need of parallel algorithms and efficient implementation. In this article, we introduce an efficient end-to-end graphical processing unit (GPU)-accelerated algorithm that 1) builds the MDP model (computing transition probabilities and expected one-step rewards) and 2) solves the MDP to compute an optimal policy. We develop methodical and algorithmic solutions to overcome the limited global memory of GPUs by 1) using a dynamic reduced-order representation of the ocean flows;2) leveraging the sparse nature of the state transition probability matrix;3) introducing a neighboring subgrid concept;and 4) proving that it is sufficient to use only the stochastic scalar field's mean to compute the expected one-step rewards for missions involving energy harvesting from the environment, thereby saving memory and reducing the computational effort. We demonstrate the algorithm on a simulated stochastic dynamic environment and highlight that it builds the MDP model and computes the optimal policy 600-1000 times faster than conventional CPU implementations, making it suitable for real-time use.

关键词： Heuristic algorithms Vehicle dynamics Stochastic processes Planning Oceans Computational modeling Uncertainty Autonomous underwater vehicles parallel programming stochastic ocean modeling transition probability value iteration (VI)

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OpenMP as runtime for providing high-level stream parallelism on multi-cores

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JOURNAL OF SUPERCOMPUTING 2022年第6期78卷 7655-7676页

作者： Hoffmann, Renato B. Loff, Junior Griebler, Dalvan Fernandes, Luiz G. Pontifical Catholic Univ Rio Grande do Sul PUCRS Sch Technol Porto Alegre RS Brazil Tres de Maio Educ Soc Setrem Lab Adv Res Cloud Comp LARCC Tres De Maio Brazil

OpenMP is an industry and academic standard for parallel programming. However, using it for developing parallel stream processing applications is complex and challenging. OpenMP lacks key programming mechanisms and abstractions for this particular domain. To tackle this problem, we used a high-level parallel programming framework (named SPar) for automatically generating parallel OpenMP code. We achieved this by leveraging SPar's language and its domain-specific code annotations for simplifying the complexity and verbosity added by OpenMP in this application domain. Consequently, we implemented a new compiler algorithm in SPar for automatically generating parallel code targeting the OpenMP runtime using source-to-source code transformations. The experiments in four different stream processing applications demonstrated that the execution time of SPar was improved up to 25.42% when using the OpenMP runtime. Additionally, our abstraction over OpenMP introduced at most 1.72% execution time overhead when compared to handwritten parallel codes. Furthermore, SPar significantly reduces the total source lines of code required to express parallelism with respect to plain OpenMP parallel codes.

关键词： parallel programming Stream processing C plus plus parallel patterns Pipeline Code transformation

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parallelizable Loop Detection using Pre-trained Transformer Models for Code Understanding 24th

Parallelizable Loop Detection using Pre-trained Transformer ...

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24th International Conference on parallel and Distributed Computing, Applications and Technologies, PDCAT 2023

作者： Pornmaneerattanatri, Soratouch Takahashi, Keichi Kashiwa, Yutaro Ichikawa, Kohei Iida, Hajimu Nara Institute of Science and Technology Nara630-0192 Japan Tohoku University Sendai980-8578 Japan

ISBN: (纸本)9789819982103

parallel programming is essential to utilize multi-core processors but remains challenging because it requires extensive knowledge of both software and hardware. Various automatic parallelization tools based on static analysis have been developed to ease the development of parallel programs. However, hand-parallelized codes still outperform auto-parallelized codes. Meanwhile, transformer-based large language models have made ground-breaking progress in coder understanding and generation tasks. In this paper, we fine-tune a transformer-based code understanding model, CodeT5, to create a model for automatically identifying parallelizable for-loops. The trained model helps developers to identify independent for-loops that can be potentially parallelized using tools such as OpenMP to improve the program performance. Our model is trained over 90,908 for-loops collected from 9 million C/C++ source files of public GitHub repositories, and achieves a 0.895 F1 score in identifying parallelizable for-loops in public GitHub projects and a 0.713 F1 score in the NAS parallel Benchmark suite. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： parallel programming

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Improving the MPI Remote Memory Access Model for Distributed-memory Systems by Implementing One-sided Broadcast 27

Improving the MPI Remote Memory Access Model for Distributed...

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27th International Conference on Soft Computing and Measurements, SCM 2024

作者： Abuelsoud, Mohamed Paznikov, Aleksej A. Saint Petersburg Electrotechnical University 'LETI' Department of Computer Science and Engineering Saint Petersburg Russia

ISBN: (纸本)9798350363708

Currently, processing large volumes of expanding data efficiently and consistently is a significant challenge. Traditional distributed-memory high-performance computers (HPC) based on message-passing model struggle with inherent synchronization difficulties, limiting their ability to keep pace. Remote Memory Access (RMA, also known as one-sided MPI communications) allows a process to directly read from or write to the memory of another process, bypassing the need for message exchange. Unfortunately, there is no collective operation interface in the current MPI RMA standard. However, RMA has the potential to reduce synchronization costs by enabling concurrent access to shared data structures, distributed among MPI processes' memories. Existing onesided MPI standards offer a linear interface only that hampers parallelization and far from efficient. To bridge this gap, we propose an algorithm design for efficient collective (parallelizable) operations in the RMA paradigm. Our study primarily examines the benefits of collective operations using the broadcast algorithm as an example. Our implementations surpass traditional methods, demonstrating the promising potential of this technique, as more performance tests indicate. © 2024 IEEE.

关键词： parallel programming

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