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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/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/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. Camera-ready version for FTXS'24 (https://sites.google.com/view/ftxs2024)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.17710">arXiv:2406.17710</a> <span> [<a href="https://arxiv.org/pdf/2406.17710">pdf</a>, <a href="https://arxiv.org/format/2406.17710">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"> GreenFaaS: Maximizing Energy Efficiency of HPC Workloads with FaaS </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=Hayot-Sasson%2C+V">Valerie Hayot-Sasson</a>, <a href="/search/cs?searchtype=author&query=Babuji%2C+Y">Yadu Babuji</a>, <a href="/search/cs?searchtype=author&query=Bauer%2C+A">Andre Bauer</a>, <a href="/search/cs?searchtype=author&query=Rattihalli%2C+G">Gourav Rattihalli</a>, <a href="/search/cs?searchtype=author&query=Hogade%2C+N">Ninad Hogade</a>, <a href="/search/cs?searchtype=author&query=Milojicic%2C+D">Dejan Milojicic</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="2406.17710v1-abstract-short" style="display: inline;"> Application energy efficiency can be improved by executing each application component on the compute element that consumes the least energy while also satisfying time constraints. In principle, the function as a service (FaaS) paradigm should simplify such optimizations by abstracting away compute location, but existing FaaS systems do not provide for user transparency over application energy cons… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.17710v1-abstract-full').style.display = 'inline'; document.getElementById('2406.17710v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.17710v1-abstract-full" style="display: none;"> Application energy efficiency can be improved by executing each application component on the compute element that consumes the least energy while also satisfying time constraints. In principle, the function as a service (FaaS) paradigm should simplify such optimizations by abstracting away compute location, but existing FaaS systems do not provide for user transparency over application energy consumption or task placement. Here we present GreenFaaS, a novel open source framework that bridges this gap between energy-efficient applications and FaaS platforms. GreenFaaS can be deployed by end users or providers across systems to monitor energy use, provide task-specific feedback, and schedule tasks in an energy-aware manner. We demonstrate that intelligent placement of tasks can both reduce energy consumption and improve performance. For a synthetic workload, GreenFaaS reduces the energy-delay product by 45% compared to alternatives. Furthermore, running a molecular design application through GreenFaaS can reduce energy consumption by 21% and runtime by 63% by better matching tasks with machines. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.17710v1-abstract-full').style.display = 'none'; document.getElementById('2406.17710v1-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> 25 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 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">11 pages, 10 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.03480">arXiv:2402.03480</a> <span> [<a href="https://arxiv.org/pdf/2402.03480">pdf</a>, <a href="https://arxiv.org/format/2402.03480">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> Trillion Parameter AI Serving Infrastructure for Scientific Discovery: A Survey and Vision </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Hudson%2C+N">Nathaniel Hudson</a>, <a href="/search/cs?searchtype=author&query=Pauloski%2C+J+G">J. Gregory Pauloski</a>, <a href="/search/cs?searchtype=author&query=Baughman%2C+M">Matt Baughman</a>, <a href="/search/cs?searchtype=author&query=Kamatar%2C+A">Alok Kamatar</a>, <a href="/search/cs?searchtype=author&query=Sakarvadia%2C+M">Mansi Sakarvadia</a>, <a href="/search/cs?searchtype=author&query=Ward%2C+L">Logan Ward</a>, <a href="/search/cs?searchtype=author&query=Chard%2C+R">Ryan Chard</a>, <a href="/search/cs?searchtype=author&query=Bauer%2C+A">Andr茅 Bauer</a>, <a href="/search/cs?searchtype=author&query=Levental%2C+M">Maksim Levental</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+W">Wenyi Wang</a>, <a href="/search/cs?searchtype=author&query=Engler%2C+W">Will Engler</a>, <a href="/search/cs?searchtype=author&query=Skelly%2C+O+P">Owen Price Skelly</a>, <a href="/search/cs?searchtype=author&query=Blaiszik%2C+B">Ben Blaiszik</a>, <a href="/search/cs?searchtype=author&query=Stevens%2C+R">Rick Stevens</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="2402.03480v1-abstract-short" style="display: inline;"> Deep learning methods are transforming research, enabling new techniques, and ultimately leading to new discoveries. As the demand for more capable AI models continues to grow, we are now entering an era of Trillion Parameter Models (TPM), or models with more than a trillion parameters -- such as Huawei's PanGu-$危$. We describe a vision for the ecosystem of TPM users and providers that caters to t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.03480v1-abstract-full').style.display = 'inline'; document.getElementById('2402.03480v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.03480v1-abstract-full" style="display: none;"> Deep learning methods are transforming research, enabling new techniques, and ultimately leading to new discoveries. As the demand for more capable AI models continues to grow, we are now entering an era of Trillion Parameter Models (TPM), or models with more than a trillion parameters -- such as Huawei's PanGu-$危$. We describe a vision for the ecosystem of TPM users and providers that caters to the specific needs of the scientific community. We then outline the significant technical challenges and open problems in system design for serving TPMs to enable scientific research and discovery. Specifically, we describe the requirements of a comprehensive software stack and interfaces to support the diverse and flexible requirements of researchers. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.03480v1-abstract-full').style.display = 'none'; document.getElementById('2402.03480v1-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> 5 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 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">10 pages, 3 figures, accepted for publication in the proceedings of the 10th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT2023)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2307.16080">arXiv:2307.16080</a> <span> [<a href="https://arxiv.org/pdf/2307.16080">pdf</a>, <a href="https://arxiv.org/format/2307.16080">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Programming Languages">cs.PL</span> </div> </div> <p class="title is-5 mathjax"> nelli: a lightweight frontend for MLIR </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Levental%2C+M">Maksim Levental</a>, <a href="/search/cs?searchtype=author&query=Kamatar%2C+A">Alok Kamatar</a>, <a href="/search/cs?searchtype=author&query=Chard%2C+R">Ryan Chard</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="2307.16080v2-abstract-short" style="display: inline;"> Multi-Level Intermediate Representation (MLIR) is a novel compiler infrastructure that aims to provide modular and extensible components to facilitate building domain specific compilers. However, since MLIR models programs at an intermediate level of abstraction, and most extant frontends are at a very high level of abstraction, the semantics and mechanics of the fundamental transformations availa… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.16080v2-abstract-full').style.display = 'inline'; document.getElementById('2307.16080v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2307.16080v2-abstract-full" style="display: none;"> Multi-Level Intermediate Representation (MLIR) is a novel compiler infrastructure that aims to provide modular and extensible components to facilitate building domain specific compilers. However, since MLIR models programs at an intermediate level of abstraction, and most extant frontends are at a very high level of abstraction, the semantics and mechanics of the fundamental transformations available in MLIR are difficult to investigate and employ in and of themselves. To address these challenges, we have developed \texttt{nelli}, a lightweight, Python-embedded, domain-specific, language for generating MLIR code. \texttt{nelli} leverages existing MLIR infrastructure to develop Pythonic syntax and semantics for various MLIR features. We describe \texttt{nelli}'s design goals, discuss key details of our implementation, and demonstrate how \texttt{nelli} enables easily defining and lowering compute kernels to diverse hardware platforms. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.16080v2-abstract-full').style.display = 'none'; document.getElementById('2307.16080v2-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> 14 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 29 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2211.04194">arXiv:2211.04194</a> <span> [<a href="https://arxiv.org/pdf/2211.04194">pdf</a>, <a href="https://arxiv.org/format/2211.04194">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Submission-Aware Reviewer Profiling for Reviewer Recommender System </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Anjum%2C+O">Omer Anjum</a>, <a href="/search/cs?searchtype=author&query=Kamatar%2C+A">Alok Kamatar</a>, <a href="/search/cs?searchtype=author&query=Liang%2C+T">Toby Liang</a>, <a href="/search/cs?searchtype=author&query=Xiong%2C+J">Jinjun Xiong</a>, <a href="/search/cs?searchtype=author&query=Hwu%2C+W">Wen-mei Hwu</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="2211.04194v1-abstract-short" style="display: inline;"> Assigning qualified, unbiased and interested reviewers to paper submissions is vital for maintaining the integrity and quality of the academic publishing system and providing valuable reviews to authors. However, matching thousands of submissions with thousands of potential reviewers within a limited time is a daunting challenge for a conference program committee. Prior efforts based on topic mode… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.04194v1-abstract-full').style.display = 'inline'; document.getElementById('2211.04194v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2211.04194v1-abstract-full" style="display: none;"> Assigning qualified, unbiased and interested reviewers to paper submissions is vital for maintaining the integrity and quality of the academic publishing system and providing valuable reviews to authors. However, matching thousands of submissions with thousands of potential reviewers within a limited time is a daunting challenge for a conference program committee. Prior efforts based on topic modeling have suffered from losing the specific context that help define the topics in a publication or submission abstract. Moreover, in some cases, topics identified are difficult to interpret. We propose an approach that learns from each abstract published by a potential reviewer the topics studied and the explicit context in which the reviewer studied the topics. Furthermore, we contribute a new dataset for evaluating reviewer matching systems. Our experiments show a significant, consistent improvement in precision when compared with the existing methods. We also use examples to demonstrate why our recommendations are more explainable. The new approach has been deployed successfully at top-tier conferences in the last two years. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.04194v1-abstract-full').style.display = 'none'; document.getElementById('2211.04194v1-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> 8 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2022. </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a 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