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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/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/2302.06751">arXiv:2302.06751</a> <span> [<a href="https://arxiv.org/pdf/2302.06751">pdf</a>, <a href="https://arxiv.org/format/2302.06751">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Hardware Architecture">cs.AR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> OpenHLS: High-Level Synthesis for Low-Latency Deep Neural Networks for Experimental Science </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=Khan%2C+A">Arham Khan</a>, <a href="/search/cs?searchtype=author&query=Chard%2C+R">Ryan Chard</a>, <a href="/search/cs?searchtype=author&query=Yoshii%2C+K">Kazutomo Yoshii</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="2302.06751v4-abstract-short" style="display: inline;"> In many experiment-driven scientific domains, such as high-energy physics, material science, and cosmology, high data rate experiments impose hard constraints on data acquisition systems: collected data must either be indiscriminately stored for post-processing and analysis, thereby necessitating large storage capacity, or accurately filtered in real-time, thereby necessitating low-latency process… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.06751v4-abstract-full').style.display = 'inline'; document.getElementById('2302.06751v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2302.06751v4-abstract-full" style="display: none;"> In many experiment-driven scientific domains, such as high-energy physics, material science, and cosmology, high data rate experiments impose hard constraints on data acquisition systems: collected data must either be indiscriminately stored for post-processing and analysis, thereby necessitating large storage capacity, or accurately filtered in real-time, thereby necessitating low-latency processing. Deep neural networks, effective in other filtering tasks, have not been widely employed in such data acquisition systems, due to design and deployment difficulties. We present an open source, lightweight, compiler framework, without any proprietary dependencies, OpenHLS, based on high-level synthesis techniques, for translating high-level representations of deep neural networks to low-level representations, suitable for deployment to near-sensor devices such as field-programmable gate arrays. We evaluate OpenHLS on various workloads and present a case-study implementation of a deep neural network for Bragg peak detection in the context of high-energy diffraction microscopy. We show OpenHLS is able to produce an implementation of the network with a throughput 4.8 $渭$s/sample, which is approximately a 4$\times$ improvement over the existing implementation <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.06751v4-abstract-full').style.display = 'none'; document.getElementById('2302.06751v4-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> 15 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 13 February, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2205.01476">arXiv:2205.01476</a> <span> [<a href="https://arxiv.org/pdf/2205.01476">pdf</a>, <a href="https://arxiv.org/format/2205.01476">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> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Real-Time Streaming and Event-driven Control of Scientific Experiments </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Elias%2C+J+R">Jakob R. Elias</a>, <a href="/search/cs?searchtype=author&query=Chard%2C+R">Ryan Chard</a>, <a href="/search/cs?searchtype=author&query=Levental%2C+M">Maksim Levental</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+Z">Zhengchun Liu</a>, <a href="/search/cs?searchtype=author&query=Foster%2C+I">Ian Foster</a>, <a href="/search/cs?searchtype=author&query=Chaudhuri%2C+S">Santanu Chaudhuri</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="2205.01476v1-abstract-short" style="display: inline;"> Advancements in scientific instrument sensors and connected devices provide unprecedented insight into ongoing experiments and present new opportunities for control, optimization, and steering. However, the diversity of sensors and heterogeneity of their data result in make it challenging to fully realize these new opportunities. Organizing and synthesizing diverse data streams in near-real-time r… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.01476v1-abstract-full').style.display = 'inline'; document.getElementById('2205.01476v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2205.01476v1-abstract-full" style="display: none;"> Advancements in scientific instrument sensors and connected devices provide unprecedented insight into ongoing experiments and present new opportunities for control, optimization, and steering. However, the diversity of sensors and heterogeneity of their data result in make it challenging to fully realize these new opportunities. Organizing and synthesizing diverse data streams in near-real-time requires both rich automation and Machine Learning (ML). To efficiently utilize ML during an experiment, the entire ML lifecycle must be addressed, including refining experiment configurations, retraining models, and applying decisions-tasks that require an equally diverse array of computational resources spanning centralized HPC to the accelerators at the edge. Here we present the Manufacturing Data and Machine Learning platform (MDML). The MDML is designed to standardize the research and operational environment for advanced data analytics and ML-enabled automated process optimization by providing the cyberinfrastructure to integrate sensor data streams and AI in cyber-physical systems for in-situ analysis. To achieve this, the MDML provides a fabric to receive and aggregate IoT data and simultaneously orchestrate remote computation across the computing continuum. In this paper we describe the MDML and show how it is used in advanced manufacturing to act on IoT data and orchestrate distributed ML to guide experiments. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.01476v1-abstract-full').style.display = 'none'; document.getElementById('2205.01476v1-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> 3 May, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2203.00448">arXiv:2203.00448</a> <span> [<a href="https://arxiv.org/pdf/2203.00448">pdf</a>, <a href="https://arxiv.org/format/2203.00448">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="Performance">cs.PF</span> </div> </div> <p class="title is-5 mathjax"> Memory Planning for Deep Neural Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Levental%2C+M">Maksim Levental</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="2203.00448v1-abstract-short" style="display: inline;"> We study memory allocation patterns in DNNs during inference, in the context of large-scale systems. We observe that such memory allocation patterns, in the context of multi-threading, are subject to high latencies, due to \texttt{mutex} contention in the system memory allocator. Latencies incurred due to such \texttt{mutex} contention produce undesirable bottlenecks in user-facing services. Thus,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.00448v1-abstract-full').style.display = 'inline'; document.getElementById('2203.00448v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2203.00448v1-abstract-full" style="display: none;"> We study memory allocation patterns in DNNs during inference, in the context of large-scale systems. We observe that such memory allocation patterns, in the context of multi-threading, are subject to high latencies, due to \texttt{mutex} contention in the system memory allocator. Latencies incurred due to such \texttt{mutex} contention produce undesirable bottlenecks in user-facing services. Thus, we propose a "memorization" based technique, \texttt{MemoMalloc}, for optimizing overall latency, with only moderate increases in peak memory usage. Specifically, our technique consists of a runtime component, which captures all allocations and uniquely associates them with their high-level source operation, and a static analysis component, which constructs an efficient allocation "plan". We present an implementation of \texttt{MemoMalloc} in the PyTorch deep learning framework and evaluate memory consumption and execution performance on a wide range of DNN architectures. We find that \texttt{MemoMalloc} outperforms state-of-the-art general purpose memory allocators, with respect to DNN inference latency, by as much as 40\%. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.00448v1-abstract-full').style.display = 'none'; document.getElementById('2203.00448v1-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> 23 February, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2022. </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">MS Thesis</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2108.12050">arXiv:2108.12050</a> <span> [<a href="https://arxiv.org/pdf/2108.12050">pdf</a>, <a href="https://arxiv.org/format/2108.12050">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Ultrafast Focus Detection for Automated Microscopy </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=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>, <a href="/search/cs?searchtype=author&query=Wildenberg%2C+G+A">Gregg A. Wildenberg</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="2108.12050v3-abstract-short" style="display: inline;"> Technological advancements in modern scientific instruments, such as scanning electron microscopes (SEMs), have significantly increased data acquisition rates and image resolutions enabling new questions to be explored; however, the resulting data volumes and velocities, combined with automated experiments, are quickly overwhelming scientists as there remain crucial steps that require human interv… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.12050v3-abstract-full').style.display = 'inline'; document.getElementById('2108.12050v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2108.12050v3-abstract-full" style="display: none;"> Technological advancements in modern scientific instruments, such as scanning electron microscopes (SEMs), have significantly increased data acquisition rates and image resolutions enabling new questions to be explored; however, the resulting data volumes and velocities, combined with automated experiments, are quickly overwhelming scientists as there remain crucial steps that require human intervention, for example reviewing image focus. We present a fast out-of-focus detection algorithm for electron microscopy images collected serially and demonstrate that it can be used to provide near-real-time quality control for neuroscience workflows. Our technique, \textit{Multi-scale Histologic Feature Detection}, adapts classical computer vision techniques and is based on detecting various fine-grained histologic features. We exploit the inherent parallelism in the technique to employ GPU primitives in order to accelerate characterization. We show that our method can detect of out-of-focus conditions within just 20ms. To make these capabilities generally available, we deploy our feature detector as an on-demand service and show that it can be used to determine the degree of focus in approximately 230ms, enabling near-real-time use. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.12050v3-abstract-full').style.display = 'none'; document.getElementById('2108.12050v3-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> 22 February, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 26 August, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2108.06831">arXiv:2108.06831</a> <span> [<a href="https://arxiv.org/pdf/2108.06831">pdf</a>, <a href="https://arxiv.org/format/2108.06831">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantum Physics">quant-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Emerging Technologies">cs.ET</span> </div> </div> <p class="title is-5 mathjax"> Tensor Networks for Simulating Quantum Circuits on FPGAs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Levental%2C+M">Maksim Levental</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="2108.06831v1-abstract-short" style="display: inline;"> Most research in quantum computing today is performed against simulations of quantum computers rather than true quantum computers. Simulating a quantum computer entails implementing all of the unitary operators corresponding to the quantum gates as tensors. For high numbers of qubits, performing tensor multiplications for these simulations becomes quite expensive, since $N$-qubit gates correspond… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.06831v1-abstract-full').style.display = 'inline'; document.getElementById('2108.06831v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2108.06831v1-abstract-full" style="display: none;"> Most research in quantum computing today is performed against simulations of quantum computers rather than true quantum computers. Simulating a quantum computer entails implementing all of the unitary operators corresponding to the quantum gates as tensors. For high numbers of qubits, performing tensor multiplications for these simulations becomes quite expensive, since $N$-qubit gates correspond to $2^{N}$-dimensional tensors. One way to accelerate such a simulation is to use field programmable gate array (FPGA) hardware to efficiently compute the matrix multiplications. Though FPGAs can efficiently perform tensor multiplications, they are memory bound, having relatively small block random access memory. One way to potentially reduce the memory footprint of a quantum computing system is to represent it as a tensor network; tensor networks are a formalism for representing compositions of tensors wherein economical tensor contractions are readily identified. Thus we explore tensor networks as a means to reducing the memory footprint of quantum computing systems and broadly accelerating simulations of such systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.06831v1-abstract-full').style.display = 'none'; document.getElementById('2108.06831v1-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> 15 August, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2012.08545">arXiv:2012.08545</a> <span> [<a href="https://arxiv.org/pdf/2012.08545">pdf</a>, <a href="https://arxiv.org/format/2012.08545">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="General Relativity and Quantum Cosmology">gr-qc</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Instrumentation and Methods for Astrophysics">astro-ph.IM</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 class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1038/s41550-021-01405-0">10.1038/s41550-021-01405-0 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Accelerated, Scalable and Reproducible AI-driven Gravitational Wave Detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Huerta%2C+E+A">E. A. Huerta</a>, <a href="/search/cs?searchtype=author&query=Khan%2C+A">Asad Khan</a>, <a href="/search/cs?searchtype=author&query=Huang%2C+X">Xiaobo Huang</a>, <a href="/search/cs?searchtype=author&query=Tian%2C+M">Minyang Tian</a>, <a href="/search/cs?searchtype=author&query=Levental%2C+M">Maksim Levental</a>, <a href="/search/cs?searchtype=author&query=Chard%2C+R">Ryan Chard</a>, <a href="/search/cs?searchtype=author&query=Wei%2C+W">Wei Wei</a>, <a href="/search/cs?searchtype=author&query=Heflin%2C+M">Maeve Heflin</a>, <a href="/search/cs?searchtype=author&query=Katz%2C+D+S">Daniel S. Katz</a>, <a href="/search/cs?searchtype=author&query=Kindratenko%2C+V">Volodymyr Kindratenko</a>, <a href="/search/cs?searchtype=author&query=Mu%2C+D">Dawei Mu</a>, <a href="/search/cs?searchtype=author&query=Blaiszik%2C+B">Ben Blaiszik</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="2012.08545v2-abstract-short" style="display: inline;"> The development of reusable artificial intelligence (AI) models for wider use and rigorous validation by the community promises to unlock new opportunities in multi-messenger astrophysics. Here we develop a workflow that connects the Data and Learning Hub for Science, a repository for publishing AI models, with the Hardware Accelerated Learning (HAL) cluster, using funcX as a universal distributed… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2012.08545v2-abstract-full').style.display = 'inline'; document.getElementById('2012.08545v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2012.08545v2-abstract-full" style="display: none;"> The development of reusable artificial intelligence (AI) models for wider use and rigorous validation by the community promises to unlock new opportunities in multi-messenger astrophysics. Here we develop a workflow that connects the Data and Learning Hub for Science, a repository for publishing AI models, with the Hardware Accelerated Learning (HAL) cluster, using funcX as a universal distributed computing service. Using this workflow, an ensemble of four openly available AI models can be run on HAL to process an entire month's worth (August 2017) of advanced Laser Interferometer Gravitational-Wave Observatory data in just seven minutes, identifying all four all four binary black hole mergers previously identified in this dataset and reporting no misclassifications. This approach combines advances in AI, distributed computing, and scientific data infrastructure to open new pathways to conduct reproducible, accelerated, data-driven discovery. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2012.08545v2-abstract-full').style.display = 'none'; document.getElementById('2012.08545v2-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> 9 July, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 December, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2020. </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">17 pages, 5 figures; v2: 12 pages, 6 figures. Accepted to Nature Astronomy. See also the Behind the Paper blog in Nature Astronomy "https://astronomycommunity.nature.com/posts/from-disruption-to-sustained-innovation-artificial-intelligence-for-gravitational-wave-astrophysics"</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 68T01; 68T35; 83C35; 83C57 </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Nat Astron 5, 1062-1068 (2021) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2012.07163">arXiv:2012.07163</a> <span> [<a href="https://arxiv.org/pdf/2012.07163">pdf</a>, <a href="https://arxiv.org/format/2012.07163">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="Performance">cs.PF</span> </div> </div> <p class="title is-5 mathjax"> Comparing the costs of abstraction for DL frameworks </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=Orlova%2C+E">Elena Orlova</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="2012.07163v1-abstract-short" style="display: inline;"> High level abstractions for implementing, training, and testing Deep Learning (DL) models abound. Such frameworks function primarily by abstracting away the implementation details of arbitrary neural architectures, thereby enabling researchers and engineers to focus on design. In principle, such frameworks could be "zero-cost abstractions"; in practice, they incur translation and indirection overh… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2012.07163v1-abstract-full').style.display = 'inline'; document.getElementById('2012.07163v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2012.07163v1-abstract-full" style="display: none;"> High level abstractions for implementing, training, and testing Deep Learning (DL) models abound. Such frameworks function primarily by abstracting away the implementation details of arbitrary neural architectures, thereby enabling researchers and engineers to focus on design. In principle, such frameworks could be "zero-cost abstractions"; in practice, they incur translation and indirection overheads. We study at which points exactly in the engineering life-cycle of a DL model the highest costs are paid and whether they can be mitigated. We train, test, and evaluate a representative DL model using PyTorch, LibTorch, TorchScript, and cuDNN on representative datasets, comparing accuracy, execution time and memory efficiency. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2012.07163v1-abstract-full').style.display = 'none'; document.getElementById('2012.07163v1-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 December, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2010.08486">arXiv:2010.08486</a> <span> [<a href="https://arxiv.org/pdf/2010.08486">pdf</a>, <a href="https://arxiv.org/format/2010.08486">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/XLOOP51963.2020.00011">10.1109/XLOOP51963.2020.00011 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Towards Online Steering of Flame Spray Pyrolysis Nanoparticle Synthesis </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=Chard%2C+R">Ryan Chard</a>, <a href="/search/cs?searchtype=author&query=Libera%2C+J+A">Joseph A. Libera</a>, <a href="/search/cs?searchtype=author&query=Chard%2C+K">Kyle Chard</a>, <a href="/search/cs?searchtype=author&query=Koripelly%2C+A">Aarthi Koripelly</a>, <a href="/search/cs?searchtype=author&query=Elias%2C+J+R">Jakob R. Elias</a>, <a href="/search/cs?searchtype=author&query=Schwarting%2C+M">Marcus Schwarting</a>, <a href="/search/cs?searchtype=author&query=Blaiszik%2C+B">Ben Blaiszik</a>, <a href="/search/cs?searchtype=author&query=Stan%2C+M">Marius Stan</a>, <a href="/search/cs?searchtype=author&query=Chaudhuri%2C+S">Santanu Chaudhuri</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="2010.08486v1-abstract-short" style="display: inline;"> Flame Spray Pyrolysis (FSP) is a manufacturing technique to mass produce engineered nanoparticles for applications in catalysis, energy materials, composites, and more. FSP instruments are highly dependent on a number of adjustable parameters, including fuel injection rate, fuel-oxygen mixtures, and temperature, which can greatly affect the quality, quantity, and properties of the yielded nanopart… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.08486v1-abstract-full').style.display = 'inline'; document.getElementById('2010.08486v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2010.08486v1-abstract-full" style="display: none;"> Flame Spray Pyrolysis (FSP) is a manufacturing technique to mass produce engineered nanoparticles for applications in catalysis, energy materials, composites, and more. FSP instruments are highly dependent on a number of adjustable parameters, including fuel injection rate, fuel-oxygen mixtures, and temperature, which can greatly affect the quality, quantity, and properties of the yielded nanoparticles. Optimizing FSP synthesis requires monitoring, analyzing, characterizing, and modifying experimental conditions.Here, we propose a hybrid CPU-GPU Difference of Gaussians (DoG)method for characterizing the volume distribution of unburnt solution, so as to enable near-real-time optimization and steering of FSP experiments. Comparisons against standard implementations show our method to be an order of magnitude more efficient. This surrogate signal can be deployed as a component of an online end-to-end pipeline that maximizes the synthesis yield. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.08486v1-abstract-full').style.display = 'none'; document.getElementById('2010.08486v1-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 October, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2020. </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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