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检索条件"任意字段=Proceedings - 24th International Conference on Distributed Computing Systems Workshops"

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Mosaic: Detection and Categorization of I/O Patterns in HPC Applications 24

Mosaic: Detection and Categorization of I/O Patterns in HPC ...

引用

proceedings of the SC '24 workshops of the international conference on High Performance computing, Network, Storage, and Analysis

作者： théo Jolivel François Tessier Julien Monniot Guillaume Pallez Univ Rennes Inria CNRS IRISA Rennes France

ISBN: (纸本)9798350355543

With the gap between computing power and I/O performance growing ever wider on HPC systems, it is becoming crucial to optimize how applications perform I/O on storage resources. To achieve this, a good understanding of application I/O behavior is an essential preliminary step. In this paper, we introduce MOSAIC, a method for categorizing applications according to their I/O behavior. We first propose an abstraction for characterizing I/O operations in terms of periodicity, temporality and metadata access. We then present a set of segmentation-based techniques for quickly and automatically detecting meaningful data access patterns. In the end, MOSAIC is able to characterize a full set of real-world I/O traces from the Blue Waters supercomputer with 92% accuracy.

关键词： I/O

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Vendor-neutral and Production-grade Job Power Management in High Performance computing 24

Vendor-neutral and Production-grade Job Power Management in ...

引用

proceedings of the SC '24 workshops of the international conference on High Performance computing, Network, Storage, and Analysis

作者： Naman Kulshreshtha Tapasya Patki Jim Garlick Mark Grondona Rong Ge Clemson University Clemson USA Lawrence Livermore National Laboratory

ISBN: (纸本)9798350355543

Power management and energy efficiency are critical research areas for exascale computing and beyond, necessitating reliable telemetry and control for distributed systems. Despite this need, existing approaches present several limitations precluding their adoption in production. these limitations include, but are not limited to, lack of portability due to vendor-specific and closed-source solutions, lack of support for non-MPI applications, and lack of user-level *** present a job-level power management framework based on Flux. We introduce flux-power-monitor and demonstrate its effectiveness on the Lassen (IBM Power AC922) and Tioga (HPE Cray EX235A) systems with a low average overhead of 0.4%. We also present flux-power-manager, where we discuss a proportional sharing policy and introduce a hierarchical FFT-based dynamic power management algorithm (FPP). We demonstrate that FPP reduces energy by 1% compared to proportional sharing, and by 20% compared to the default IBM static power capping policy.

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Intel® SHMEM: GPU-initiated OpenSHMEM using SYCL 24

Intel® SHMEM: GPU-initiated OpenSHMEM using SYCL

引用

proceedings of the SC '24 workshops of the international conference on High Performance computing, Network, Storage, and Analysis

作者： Alex Brooks Philip Marshall David Ozog Md. Wasi-ur-Rahman Lawrence Stewart Rithwik Tom Intel Corporation

ISBN: (纸本)9798350355543

Modern high-end systems are increasingly becoming heterogeneous, providing users options to use general purpose Graphics Processing Units (GPU) and other accelerators for additional performance. High Performance computing (HPC) and Artificial Intelligence (AI) applications are often carefully arranged to overlap communications and computation for increased efficiency on such platforms. this has led to efforts to extend popular communication libraries to support GPU awareness and more recently, GPU-initiated operations. In this paper, we present Intel SHMEM, a library that enables users to write programs that are GPU aware, in that API calls support GPU memory, and also support GPU-initiated communication operations by embedding OpenSHMEM style calls within GPU kernels. We also propose thread-collaborative extensions to the OpenSHMEM standard that can enable users to better exploit the strengths of GPUs. Our implementation adapts to choose between direct load/store from GPU and the GPU copy engine based transfer to optimize performance on different configurations.

关键词： distributed Communications

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Speeding-Up LULESH on HPX: Useful Tricks and Lessons Learned using a Many-Task-Based Approach 24

Speeding-Up LULESH on HPX: Useful Tricks and Lessons Learned...

引用

proceedings of the SC '24 workshops of the international conference on High Performance computing, Network, Storage, and Analysis

作者： Torben Kalkhof Andreas Koch Embedded Systems and Applications Group Technical University of Darmstadt Darmstadt Germany

ISBN: (纸本)9798350355543

Current programming models face challenges in dealing with modern supercomputers' growing parallelism and heterogeneity. Emerging programming models, like the task-based programming model found in the asynchronous many-task HPX programming framework, offer new ways to express parallelism, enhance scalability, and mask synchronization and communication latency on multi-core and distributed *** high-performance computing benchmarks are often unsuitable for comparing different programming models due to their limited code complexity. However, real-world scientific applications are usually too complex. As a middle ground, proxy applications model the behavior of actual scientific problems, while reducing code *** our research on using HPX to program machines with heterogeneous compute units (e.g., GPU and FPGA/AI Engines), we have also substantially optimized a pure HPX-based software baseline of the LULESH proxy application. this paper discusses the techniques we applied yielding single-node speed-ups of 1.33x to 2.25x for different problem sizes relative to the LULESH OpenMP reference implementation.

关键词： HPC

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Octopus: Experiences with a Hybrid Event-Driven Architecture for distributed Scientific computing 24

Octopus: Experiences with a Hybrid Event-Driven Architecture...

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proceedings of the SC '24 workshops of the international conference on High Performance computing, Network, Storage, and Analysis

作者： Haochen Pan Ryan Chard Sicheng Zhou Alok Kamatar Rafael Vescovi Valérie Hayot-Sasson André Bauer Maxime Gonthier Kyle Chard Ian Foster Department of Computer Science University of Chicago Chicago IL USA Data Science and Learning Division Argonne National Laboratory Lemont IL USA Department of Computer Science Southern University of Science and Technology Guangdong China Department of Computer Science Illinois Institute of Technology Chicago IL USA Department of Computer Science University of Chicago Chicago IL USA and Data Science and Learning Division Argonne National Laboratory Lemont IL USA

ISBN: (纸本)9798350355543

Scientific research increasingly relies on distributed computational resources, storage systems, networks, and instruments, ranging from HPC and cloud systems to edge devices. Event-driven architecture (EDA) benefits applications targeting distributed research infrastructures by enabling the organization, communication, processing, reliability, and security of events generated from many sources. To support the development of scientific EDA, we introduce Octopus, a hybrid, cloud-to-edge event fabric designed to link many local event producers and consumers with cloud-hosted brokers, and to provide a fabric for developing resilient applications. Octopus can be scaled to meet demand, permits the deployment of highly available Triggers for automatic event processing, and enforces fine-grained access control. We identify requirements in self-driving laboratories, scientific data automation, online task scheduling, epidemic modeling, and dynamic workflow management use cases, and present results demonstrating Octopus' ability to meet those requirements. Octopus supports producing and consuming events at a rate of over 4.2 M and 9.6 M events per second, respectively, from distributed clients.

关键词： Event-driven architecture

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Scalable and Consistent Graph Neural Networks for distributed Mesh-based Data-driven Modeling 24

Scalable and Consistent Graph Neural Networks for Distribute...

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proceedings of the SC '24 workshops of the international conference on High Performance computing, Network, Storage, and Analysis

作者： Shivam Barwey Riccardo Balin Bethany Lusch Saumil Patel Ramesh Balakrishnan Pinaki Pal Romit Maulik Venkatram Vishwanath Transportation and Power Systems Argonne National Laboratory Lemont USA Leadership Computing Facility Argonne National Laboratory Lemont USA Computational Science Division Argonne National Laboratory Lemont USA Information Sciences and Technology Pennsylvania State University University Park USA Mathematics and Computer Science Argonne National Laboratory Lemont USA

ISBN: (纸本)9798350355543

this work develops a distributed graph neural network (GNN) methodology for mesh-based modeling applications using a consistent neural message passing layer. As the name implies, the focus is on enabling scalable operations that satisfy physical consistency via halo nodes at sub-graph boundaries. Here, consistency refers to the fact that a GNN trained and evaluated on one rank (one large graph) is arithmetically equivalent to evaluations on multiple ranks (a partitioned graph). this concept is demonstrated by interfacing GNNs with NekRS, a GPU-capable exascale CFD solver developed at Argonne National Laboratory. It is shown how the NekRS mesh partitioning can be linked to the distributed GNN training and inference routines, resulting in a scalable mesh-based data-driven modeling workflow. We study the impact of consistency on the scalability of mesh-based GNNs, demonstrating efficient scaling in consistent GNNs for up to O(1B) graph nodes on the Frontier exascale supercomputer.

关键词： graph neural networks

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Performance Characterization and Provenance of distributed Task-based Workflows on HPC Platforms 24

Performance Characterization and Provenance of Distributed T...

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proceedings of the SC '24 workshops of the international conference on High Performance computing, Network, Storage, and Analysis

作者： Amal Gueroudji Chase Phelps Tanzima Z. Islam Philip Carns Shane Snyder Matthieu Dorier Robert B. Ross Line C. Pouchard Argonne National Laboratory Texas State University Sandia National Laboratories

ISBN: (纸本)9798350355543

Understanding performance and provenance of task-based workflows poses significant challenges, particularly in distributed configurations where resources are shared by multiple applications. Task-based workflow management systems further complicate performance predictability because of their dynamicity that subtly alters task execution order from run to run. In this paper we propose a layered characterization framework for performance and task provenance for *** workflows running on high-performance computing (HPC) platforms. It collects data from jobs, the workflow management system, and the operating system to aid in understanding the performance of these workflows. Our approach encompasses three main contributions: first, an extension of *** to capture high-fidelity task provenance using Mochi data services; second, the adaptation of the established HPC I/O characterization tool Darshan to gather high-fidelity I/O data, thereby enhancing the granularity of our analysis; and third, a framework to combine and process the collected data and provide helpful insights into performance characterization and reproducibility, alongside our lessons learned.

关键词： Dask

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DeLiBA-K: Speeding-up Hardware-Accelerated distributed Storage Access by Tighter Linux Kernel Integration and Use of a Modern API 24

DeLiBA-K: Speeding-up Hardware-Accelerated Distributed Stora...

引用

proceedings of the SC '24 workshops of the international conference on High Performance computing, Network, Storage, and Analysis

作者： Babar Khan Andreas Koch Embedded Systems and Applications Group TU Darmstadt Germany

ISBN: (纸本)9798350355543

We present DeLiBA-K, an improved version of the Development of Linux Block I/O Accelerators (DeLiBA) framework. DeLiBA-K operates at the Linux kernel level, bypassing the user-space interactions of DeLiBA-1 and -2 to interact with the block and network I/O kernel stack directly. Another critical feature of DeLiBA-K is implementing and benchmarking the modern io_uring Asynchronous I/O (AIO) API within a 16nm AMD Alveo U280 FPGA I/O framework. this allows for better parallelism and reduced latency in I/O operations. Our results show significant performance gains, up to a 3.2x improvement in I/O operations per second (IOPS) and 3.45x increased throughput for synthetic workloads. Real-world applications see a 30% reduction in execution time for data-intensive tasks. DeLiBA-K has been successfully tested in an industrial environment using real workloads, demonstrating its effectiveness in large-scale enterprise environments.

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Autonomous Execution for Multi-GPU systems: Compiler Support 24

Autonomous Execution for Multi-GPU Systems: Compiler Support

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proceedings of the SC '24 workshops of the international conference on High Performance computing, Network, Storage, and Analysis

作者： Javid Baydamirli Tal Ben Nun Didem Unat Koç University Istanbul Turkey Lawrence Livermore National Laboratory Livermore California USA

ISBN: (纸本)9798350355543

Recent trends in HPC systems increasingly emphasize accelerators, particularly GPUs, as autonomous execution units, shifting control of entire program execution to GPUs. Communication libraries enable devices to move data independently among one another, bringing forth latency improvements, and first-party GPU runtimes expose APIs for kernels to organize their execution. Despite the trends and advancements, current high-level frameworks and compilers lack support for constructs enabling this autonomous execution. In this work, we aim to bridge this gap with a compiler and provide a productive method for writing efficient GPU-first code. We design and develop a code generator that efficiently fuses and schedules persistent kernels, provides high-level abstractions over device resources, and enables GPU-initiated communication within Python code using NVSHMEM to realize autonomous multi-GPU execution. We compare our implementation to other accelerated Python compilers including CuPy, DaCe, and cuNumeric on 22 NPBench kernels. We additionally perform a scaling study of distributed 2D/3D Jacobi and observe a speedup of 6.1x and 30.8x over DaCe and cuNumeric, respectively, on 8 GPUs for the 3D case with a scaling efficiency of 98%.

关键词： GPU-initiated communication

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Enabling Low-Overhead HT-HPC Workflows at Extreme Scale using GNU Parallel 24

Enabling Low-Overhead HT-HPC Workflows at Extreme Scale usin...

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proceedings of the SC '24 workshops of the international conference on High Performance computing, Network, Storage, and Analysis

作者： Ketan Maheshwari William Arndt Ahmad Maroof Karimi Junqi Yin Frédéric Suter Seth Johnson Rafael Ferreira da Silva Oak Ridge National Laboratory Oak Ridge TN USA Lawrence Berkeley National Laboratory Berkeley CA USA

ISBN: (纸本)9798350355543

GNU Parallel is a versatile and powerful tool for process parallelizatlon widely used in scientific computing. this paper demonstrates its effective application in high-performance computing (HPC) environments, particularly focusing on its scalability and efficiency in executing large-scale high-throughput high-performance computing (HT-HPC) workflows. through real-world examples, we highlight GNU Parallel's performance across various HPC workloads, including GPU computing, container-based workloads, and node-local NVMe storage. Our results on two leading supercomputers, OLCF's Frontier and NERSC's Perlmutter, showcase GNU Parallel's rapid process dispatching ability and its capacity to maintain low overhead even at extreme scales. We explore GNU Parallel's application in massive parallel file transfers using a scheduled Data Transfer Node (DTN) cluster, emphasizing its broad utility in diverse scientific workflows. Beyond its direct application as a viable workflow manager, GNU Parallel can be employed in conjunction with other workflow systems as a "last-mile" parallelizing driver and as a quick prototyping tool to design and extract parallel profiles from application executions. We then argue that the potential for GNU Parallel to transform workflow management at extreme scales is substantial, paving the way for more efficient and effective scientific discoveries.

关键词： distributed computing

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