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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.05293">arXiv:2502.05293</a> <span> [<a href="https://arxiv.org/pdf/2502.05293">pdf</a>, <a href="https://arxiv.org/format/2502.05293">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> Optimizing Fine-Grained Parallelism Through Dynamic Load Balancing on Multi-Socket Many-Core Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Wang%2C+W">Wenyi Wang</a>, <a href="/search/cs?searchtype=author&query=Gonthier%2C+M">Maxime Gonthier</a>, <a href="/search/cs?searchtype=author&query=Nookala%2C+P">Poornima Nookala</a>, <a href="/search/cs?searchtype=author&query=Pan%2C+H">Haochen Pan</a>, <a href="/search/cs?searchtype=author&query=Foster%2C+I">Ian Foster</a>, <a href="/search/cs?searchtype=author&query=Raicu%2C+I">Ioan Raicu</a>, <a href="/search/cs?searchtype=author&query=Chard%2C+K">Kyle Chard</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.05293v1-abstract-short" style="display: inline;"> Achieving efficient task parallelism on many-core architectures is an important challenge. The widely used GNU OpenMP implementation of the popular OpenMP parallel programming model incurs high overhead for fine-grained, short-running tasks due to time spent on runtime synchronization. In this work, we introduce and analyze three key advances that collectively achieve significant performance gains… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05293v1-abstract-full').style.display = 'inline'; document.getElementById('2502.05293v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.05293v1-abstract-full" style="display: none;"> Achieving efficient task parallelism on many-core architectures is an important challenge. The widely used GNU OpenMP implementation of the popular OpenMP parallel programming model incurs high overhead for fine-grained, short-running tasks due to time spent on runtime synchronization. In this work, we introduce and analyze three key advances that collectively achieve significant performance gains. First, we introduce XQueue, a lock-less concurrent queue implementation to replace GNU's priority task queue and remove the global task lock. Second, we develop a scalable, efficient, and hybrid lock-free/lock-less distributed tree barrier to address the high hardware synchronization overhead from GNU's centralized barrier. Third, we develop two lock-less and NUMA-aware load balancing strategies. We evaluate our implementation using Barcelona OpenMP Task Suite (BOTS) benchmarks. Results from the first and second advances demonstrate up to 1522.8$\times$ performance improvement compared to the original GNU OpenMP. Further improvements from lock-less load balancing show up to 4$\times$ improvement compared to GNU OpenMP using XQueue. Through a rich set of profiling and instrumentation tools, we are able to investigate the runtime behavior of GNU OpenMP and improve its performance on fine-grained tasks by many orders of magnitude. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05293v1-abstract-full').style.display = 'none'; document.getElementById('2502.05293v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">13 pages, 11 figures, preprint, accepted by IPDPS2025</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> D.1.3 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.09557">arXiv:2501.09557</a> <span> [<a href="https://arxiv.org/pdf/2501.09557">pdf</a>, <a href="https://arxiv.org/format/2501.09557">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> Core Hours and Carbon Credits: Incentivizing Sustainability in HPC </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kamatar%2C+A">Alok Kamatar</a>, <a href="/search/cs?searchtype=author&query=Gonthier%2C+M">Maxime Gonthier</a>, <a href="/search/cs?searchtype=author&query=Hayot-Sasson%2C+V">Valerie Hayot-Sasson</a>, <a href="/search/cs?searchtype=author&query=Bauer%2C+A">Andre Bauer</a>, <a href="/search/cs?searchtype=author&query=Copik%2C+M">Marcin Copik</a>, <a href="/search/cs?searchtype=author&query=Hoefler%2C+T">Torsten Hoefler</a>, <a href="/search/cs?searchtype=author&query=Fernandez%2C+R+C">Raul Castro Fernandez</a>, <a href="/search/cs?searchtype=author&query=Chard%2C+K">Kyle Chard</a>, <a href="/search/cs?searchtype=author&query=Foster%2C+I">Ian Foster</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.09557v1-abstract-short" style="display: inline;"> Realizing a shared responsibility between providers and consumers is critical to manage the sustainability of HPC. However, while cost may motivate efficiency improvements by infrastructure operators, broader progress is impeded by a lack of user incentives. We conduct a survey of HPC users that reveals fewer than 30 percent are aware of their energy consumption, and that energy efficiency is amon… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.09557v1-abstract-full').style.display = 'inline'; document.getElementById('2501.09557v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.09557v1-abstract-full" style="display: none;"> Realizing a shared responsibility between providers and consumers is critical to manage the sustainability of HPC. However, while cost may motivate efficiency improvements by infrastructure operators, broader progress is impeded by a lack of user incentives. We conduct a survey of HPC users that reveals fewer than 30 percent are aware of their energy consumption, and that energy efficiency is among users' lowest priority concerns. One explanation is that existing pricing models may encourage users to prioritize performance over energy efficiency. We propose two transparent multi-resource pricing schemes, Energy- and Carbon-Based Accounting, that seek to change this paradigm by incentivizing more efficient user behavior. These two schemes charge for computations based on their energy consumption or carbon footprint, respectively, rewarding users who leverage efficient hardware and software. We evaluate these two pricing schemes via simulation, in a prototype, and a user study. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.09557v1-abstract-full').style.display = 'none'; document.getElementById('2501.09557v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.07236">arXiv:2408.07236</a> <span> [<a href="https://arxiv.org/pdf/2408.07236">pdf</a>, <a href="https://arxiv.org/format/2408.07236">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> TaPS: A Performance Evaluation Suite for Task-based Execution Frameworks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Pauloski%2C+J+G">J. Gregory Pauloski</a>, <a href="/search/cs?searchtype=author&query=Hayot-Sasson%2C+V">Valerie Hayot-Sasson</a>, <a href="/search/cs?searchtype=author&query=Gonthier%2C+M">Maxime Gonthier</a>, <a href="/search/cs?searchtype=author&query=Hudson%2C+N">Nathaniel Hudson</a>, <a href="/search/cs?searchtype=author&query=Pan%2C+H">Haochen Pan</a>, <a href="/search/cs?searchtype=author&query=Zhou%2C+S">Sicheng Zhou</a>, <a href="/search/cs?searchtype=author&query=Foster%2C+I">Ian Foster</a>, <a href="/search/cs?searchtype=author&query=Chard%2C+K">Kyle Chard</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.07236v1-abstract-short" style="display: inline;"> Task-based execution frameworks, such as parallel programming libraries, computational workflow systems, and function-as-a-service platforms, enable the composition of distinct tasks into a single, unified application designed to achieve a computational goal. Task-based execution frameworks abstract the parallel execution of an application's tasks on arbitrary hardware. Research into these task ex… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.07236v1-abstract-full').style.display = 'inline'; document.getElementById('2408.07236v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.07236v1-abstract-full" style="display: none;"> Task-based execution frameworks, such as parallel programming libraries, computational workflow systems, and function-as-a-service platforms, enable the composition of distinct tasks into a single, unified application designed to achieve a computational goal. Task-based execution frameworks abstract the parallel execution of an application's tasks on arbitrary hardware. Research into these task executors has accelerated as computational sciences increasingly need to take advantage of parallel compute and/or heterogeneous hardware. However, the lack of evaluation standards makes it challenging to compare and contrast novel systems against existing implementations. Here, we introduce TaPS, the Task Performance Suite, to support continued research in parallel task executor frameworks. TaPS provides (1) a unified, modular interface for writing and evaluating applications using arbitrary execution frameworks and data management systems and (2) an initial set of reference synthetic and real-world science applications. We discuss how the design of TaPS supports the reliable evaluation of frameworks and demonstrate TaPS through a survey of benchmarks using the provided reference applications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.07236v1-abstract-full').style.display = 'none'; document.getElementById('2408.07236v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">To appear in the Proceedings of 20th IEEE International Conference on e-Science</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.11432">arXiv:2407.11432</a> <span> [<a href="https://arxiv.org/pdf/2407.11432">pdf</a>, <a href="https://arxiv.org/format/2407.11432">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> Octopus: Experiences with a Hybrid Event-Driven Architecture for Distributed Scientific Computing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Pan%2C+H">Haochen Pan</a>, <a href="/search/cs?searchtype=author&query=Chard%2C+R">Ryan Chard</a>, <a href="/search/cs?searchtype=author&query=Zhou%2C+S">Sicheng Zhou</a>, <a href="/search/cs?searchtype=author&query=Kamatar%2C+A">Alok Kamatar</a>, <a href="/search/cs?searchtype=author&query=Vescovi%2C+R">Rafael Vescovi</a>, <a href="/search/cs?searchtype=author&query=Hayot-Sasson%2C+V">Val茅rie Hayot-Sasson</a>, <a href="/search/cs?searchtype=author&query=Bauer%2C+A">Andr茅 Bauer</a>, <a href="/search/cs?searchtype=author&query=Gonthier%2C+M">Maxime Gonthier</a>, <a href="/search/cs?searchtype=author&query=Chard%2C+K">Kyle Chard</a>, <a href="/search/cs?searchtype=author&query=Foster%2C+I">Ian Foster</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.11432v2-abstract-short" style="display: inline;"> 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. T… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.11432v2-abstract-full').style.display = 'inline'; document.getElementById('2407.11432v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.11432v2-abstract-full" style="display: none;"> 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. 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. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.11432v2-abstract-full').style.display = 'none'; document.getElementById('2407.11432v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">12 pages and 8 figures. 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