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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/2403.04311">arXiv:2403.04311</a> <span> [<a href="https://arxiv.org/pdf/2403.04311">pdf</a>, <a href="https://arxiv.org/format/2403.04311">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey 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="Information Retrieval">cs.IR</span> </div> </div> <p class="title is-5 mathjax"> ALTO: An Efficient Network Orchestrator for Compound AI Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Santhanam%2C+K">Keshav Santhanam</a>, <a href="/search/cs?searchtype=author&query=Raghavan%2C+D">Deepti Raghavan</a>, <a href="/search/cs?searchtype=author&query=Rahman%2C+M+S">Muhammad Shahir Rahman</a>, <a href="/search/cs?searchtype=author&query=Venkatesh%2C+T">Thejas Venkatesh</a>, <a href="/search/cs?searchtype=author&query=Kunjal%2C+N">Neha Kunjal</a>, <a href="/search/cs?searchtype=author&query=Thaker%2C+P">Pratiksha Thaker</a>, <a href="/search/cs?searchtype=author&query=Levis%2C+P">Philip Levis</a>, <a href="/search/cs?searchtype=author&query=Zaharia%2C+M">Matei Zaharia</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="2403.04311v1-abstract-short" style="display: inline;"> We present ALTO, a network orchestrator for efficiently serving compound AI systems such as pipelines of language models. ALTO achieves high throughput and low latency by taking advantage of an optimization opportunity specific to generative language models: streaming intermediate outputs. As language models produce outputs token by token, ALTO exposes opportunities to stream intermediate outputs… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.04311v1-abstract-full').style.display = 'inline'; document.getElementById('2403.04311v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.04311v1-abstract-full" style="display: none;"> We present ALTO, a network orchestrator for efficiently serving compound AI systems such as pipelines of language models. ALTO achieves high throughput and low latency by taking advantage of an optimization opportunity specific to generative language models: streaming intermediate outputs. As language models produce outputs token by token, ALTO exposes opportunities to stream intermediate outputs between stages when possible. We highlight two new challenges of correctness and load balancing which emerge when streaming intermediate data across distributed pipeline stage instances. We also motivate the need for an aggregation-aware routing interface and distributed prompt-aware scheduling to address these challenges. We demonstrate the impact of ALTO's partial output streaming on a complex chatbot verification pipeline, increasing throughput by up to 3x for a fixed latency target of 4 seconds / request while also reducing tail latency by 1.8x compared to a baseline serving approach. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.04311v1-abstract-full').style.display = 'none'; document.getElementById('2403.04311v1-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 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2108.12720">arXiv:2108.12720</a> <span> [<a href="https://arxiv.org/pdf/2108.12720">pdf</a>, <a href="https://arxiv.org/format/2108.12720">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Graphics">cs.GR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Multimedia">cs.MM</span> </div> </div> <p class="title is-5 mathjax"> Towards Retina-Quality VR Video Streaming: 15ms Could Save You 80% of Your Bandwidth </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Hsiao%2C+L">Luke Hsiao</a>, <a href="/search/cs?searchtype=author&query=Krajancich%2C+B">Brooke Krajancich</a>, <a href="/search/cs?searchtype=author&query=Levis%2C+P">Philip Levis</a>, <a href="/search/cs?searchtype=author&query=Wetzstein%2C+G">Gordon Wetzstein</a>, <a href="/search/cs?searchtype=author&query=Winstein%2C+K">Keith Winstein</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="2108.12720v3-abstract-short" style="display: inline;"> Virtual reality systems today cannot yet stream immersive, retina-quality virtual reality video over a network. One of the greatest challenges to this goal is the sheer data rates required to transmit retina-quality video frames at high resolutions and frame rates. Recent work has leveraged the decay of visual acuity in human perception in novel gaze-contingent video compression techniques. In thi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.12720v3-abstract-full').style.display = 'inline'; document.getElementById('2108.12720v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2108.12720v3-abstract-full" style="display: none;"> Virtual reality systems today cannot yet stream immersive, retina-quality virtual reality video over a network. One of the greatest challenges to this goal is the sheer data rates required to transmit retina-quality video frames at high resolutions and frame rates. Recent work has leveraged the decay of visual acuity in human perception in novel gaze-contingent video compression techniques. In this paper, we show that reducing the motion-to-photon latency of a system itself is a key method for improving the compression ratio of gaze-contingent compression. Our key finding is that a client and streaming server system with sub-15ms latency can achieve 5x better compression than traditional techniques while also using simpler software algorithms than previous work. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.12720v3-abstract-full').style.display = 'none'; document.getElementById('2108.12720v3-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> 20 September, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 28 August, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2021. </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">6 pages, 7 figures; added additional discussion and clarifications; updated acknowledgements</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2008.10569">arXiv:2008.10569</a> <span> [<a href="https://arxiv.org/pdf/2008.10569">pdf</a>, <a href="https://arxiv.org/format/2008.10569">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Databases">cs.DB</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/10.14778/3407790.3407848">10.14778/3407790.3407848 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Approximate Partition Selection for Big-Data Workloads using Summary Statistics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Rong%2C+K">Kexin Rong</a>, <a href="/search/cs?searchtype=author&query=Lu%2C+Y">Yao Lu</a>, <a href="/search/cs?searchtype=author&query=Bailis%2C+P">Peter Bailis</a>, <a href="/search/cs?searchtype=author&query=Kandula%2C+S">Srikanth Kandula</a>, <a href="/search/cs?searchtype=author&query=Levis%2C+P">Philip Levis</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="2008.10569v1-abstract-short" style="display: inline;"> Many big-data clusters store data in large partitions that support access at a coarse, partition-level granularity. As a result, approximate query processing via row-level sampling is inefficient, often requiring reads of many partitions. In this work, we seek to answer queries quickly and approximately by reading a subset of the data partitions and combining partial answers in a weighted manner w… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.10569v1-abstract-full').style.display = 'inline'; document.getElementById('2008.10569v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2008.10569v1-abstract-full" style="display: none;"> Many big-data clusters store data in large partitions that support access at a coarse, partition-level granularity. As a result, approximate query processing via row-level sampling is inefficient, often requiring reads of many partitions. In this work, we seek to answer queries quickly and approximately by reading a subset of the data partitions and combining partial answers in a weighted manner without modifying the data layout. We illustrate how to efficiently perform this query processing using a set of pre-computed summary statistics, which inform the choice of partitions and weights. We develop novel means of using the statistics to assess the similarity and importance of partitions. Our experiments on several datasets and data layouts demonstrate that to achieve the same relative error compared to uniform partition sampling, our techniques offer from 2.7$\times$ to $70\times$ reduction in the number of partitions read, and the statistics stored per partition require fewer than 100KB. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.10569v1-abstract-full').style.display = 'none'; document.getElementById('2008.10569v1-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> 24 August, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2007.13828">arXiv:2007.13828</a> <span> [<a href="https://arxiv.org/pdf/2007.13828">pdf</a>, <a href="https://arxiv.org/format/2007.13828">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> </div> </div> <p class="title is-5 mathjax"> GRIP: A Graph Neural Network Accelerator Architecture </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kiningham%2C+K">Kevin Kiningham</a>, <a href="/search/cs?searchtype=author&query=Re%2C+C">Christopher Re</a>, <a href="/search/cs?searchtype=author&query=Levis%2C+P">Philip Levis</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="2007.13828v2-abstract-short" style="display: inline;"> We present GRIP, a graph neural network accelerator architecture designed for low-latency inference. AcceleratingGNNs is challenging because they combine two distinct types of computation: arithmetic-intensive vertex-centric operations and memory-intensive edge-centric operations. GRIP splits GNN inference into a fixed set of edge- and vertex-centric execution phases that can be implemented in har… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.13828v2-abstract-full').style.display = 'inline'; document.getElementById('2007.13828v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2007.13828v2-abstract-full" style="display: none;"> We present GRIP, a graph neural network accelerator architecture designed for low-latency inference. AcceleratingGNNs is challenging because they combine two distinct types of computation: arithmetic-intensive vertex-centric operations and memory-intensive edge-centric operations. GRIP splits GNN inference into a fixed set of edge- and vertex-centric execution phases that can be implemented in hardware. We then specialize each unit for the unique computational structure found in each phase.For vertex-centric phases, GRIP uses a high performance matrix multiply engine coupled with a dedicated memory subsystem for weights to improve reuse. For edge-centric phases, GRIP use multiple parallel prefetch and reduction engines to alleviate the irregularity in memory accesses. Finally, GRIP supports severalGNN optimizations, including a novel optimization called vertex-tiling which increases the reuse of weight data.We evaluate GRIP by performing synthesis and place and route for a 28nm implementation capable of executing inference for several widely-used GNN models (GCN, GraphSAGE, G-GCN, and GIN). Across several benchmark graphs, it reduces 99th percentile latency by a geometric mean of 17x and 23x compared to a CPU and GPU baseline, respectively, while drawing only 5W. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.13828v2-abstract-full').style.display = 'none'; document.getElementById('2007.13828v2-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> 30 July, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 27 July, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1906.01113">arXiv:1906.01113</a> <span> [<a href="https://arxiv.org/pdf/1906.01113">pdf</a>, <a href="https://arxiv.org/format/1906.01113">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> Learning in situ: a randomized experiment in video streaming </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Yan%2C+F+Y">Francis Y. Yan</a>, <a href="/search/cs?searchtype=author&query=Ayers%2C+H">Hudson Ayers</a>, <a href="/search/cs?searchtype=author&query=Zhu%2C+C">Chenzhi Zhu</a>, <a href="/search/cs?searchtype=author&query=Fouladi%2C+S">Sadjad Fouladi</a>, <a href="/search/cs?searchtype=author&query=Hong%2C+J">James Hong</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+K">Keyi Zhang</a>, <a href="/search/cs?searchtype=author&query=Levis%2C+P">Philip Levis</a>, <a href="/search/cs?searchtype=author&query=Winstein%2C+K">Keith Winstein</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="1906.01113v2-abstract-short" style="display: inline;"> We describe the results of a randomized controlled trial of video-streaming algorithms for bitrate selection and network prediction. Over the last eight months, we have streamed 14.2 years of video to 56,000 users across the Internet. Sessions are randomized in blinded fashion among algorithms, and client telemetry is recorded for analysis. We found that in this real-world setting, it is difficu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1906.01113v2-abstract-full').style.display = 'inline'; document.getElementById('1906.01113v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1906.01113v2-abstract-full" style="display: none;"> We describe the results of a randomized controlled trial of video-streaming algorithms for bitrate selection and network prediction. Over the last eight months, we have streamed 14.2 years of video to 56,000 users across the Internet. Sessions are randomized in blinded fashion among algorithms, and client telemetry is recorded for analysis. We found that in this real-world setting, it is difficult for sophisticated or machine-learned control schemes to outperform a "simple" scheme (buffer-based control), notwithstanding good performance in network emulators or simulators. We performed a statistical analysis and found that the variability and heavy-tailed nature of network and algorithm behavior create hurdles for robust learned algorithms in this area. We developed an ABR algorithm that robustly outperforms other schemes in practice, by combining classical control with a learned network predictor, trained with supervised learning in situ on data from the real deployment environment. To support further investigation, we are publishing an archive of traces and results each day, and will open our ongoing study to the community. We welcome other researchers to use this platform to develop and validate new algorithms for bitrate selection, network prediction, and congestion control. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1906.01113v2-abstract-full').style.display = 'none'; document.getElementById('1906.01113v2-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 September, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 3 June, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> USENIX NSDI (2020) 495-511 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1806.10751">arXiv:1806.10751</a> <span> [<a href="https://arxiv.org/pdf/1806.10751">pdf</a>, <a href="https://arxiv.org/format/1806.10751">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> Design Considerations for Low Power Internet Protocols </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ayers%2C+H">Hudson Ayers</a>, <a href="/search/cs?searchtype=author&query=Crews%2C+P">Paul Crews</a>, <a href="/search/cs?searchtype=author&query=Teo%2C+H">Hubert Teo</a>, <a href="/search/cs?searchtype=author&query=McAvity%2C+C">Conor McAvity</a>, <a href="/search/cs?searchtype=author&query=Levy%2C+A">Amit Levy</a>, <a href="/search/cs?searchtype=author&query=Levis%2C+P">Philip Levis</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="1806.10751v2-abstract-short" style="display: inline;"> Over the past 10 years, low-power wireless networks have transitioned to supporting IPv6 connectivity through 6LoWPAN, a set of standards which specify how to aggressively compress IPv6 packets over low-power wireless links such as 802.15.4. We find that different low-power IPv6 stacks are unable to communicate using 6LoWPAN, and therefore IP, due to design tradeoffs between code size and energy… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1806.10751v2-abstract-full').style.display = 'inline'; document.getElementById('1806.10751v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1806.10751v2-abstract-full" style="display: none;"> Over the past 10 years, low-power wireless networks have transitioned to supporting IPv6 connectivity through 6LoWPAN, a set of standards which specify how to aggressively compress IPv6 packets over low-power wireless links such as 802.15.4. We find that different low-power IPv6 stacks are unable to communicate using 6LoWPAN, and therefore IP, due to design tradeoffs between code size and energy efficiency. We argue that applying traditional protocol design principles to low-power networks is responsible for these failures, in part because receivers must accommodate a wide range of senders. Based on these findings, we propose three design principles for Internet protocols on low-power networks. These principles are based around the importance of providing flexible tradeoffs between code size and energy efficiency. We apply these principles to 6LoWPAN and show that the resulting design of the protocol provides developers a wide range of tradeoff points while allowing implementations with different choices to seamlessly communicate. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1806.10751v2-abstract-full').style.display = 'none'; document.getElementById('1806.10751v2-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> 21 January, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 27 June, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1806.00555">arXiv:1806.00555</a> <span> [<a href="https://arxiv.org/pdf/1806.00555">pdf</a>, <a href="https://arxiv.org/format/1806.00555">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</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/IOTAIS.2018.8600854">10.1109/IOTAIS.2018.8600854 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Smart Contracts for Machine-to-Machine Communication: Possibilities and Limitations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Hanada%2C+Y">Yuichi Hanada</a>, <a href="/search/cs?searchtype=author&query=Hsiao%2C+L">Luke Hsiao</a>, <a href="/search/cs?searchtype=author&query=Levis%2C+P">Philip Levis</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="1806.00555v2-abstract-short" style="display: inline;"> Blockchain technologies, such as smart contracts, present a unique interface for machine-to-machine communication that provides a secure, append-only record that can be shared without trust and without a central administrator. We study the possibilities and limitations of using smart contracts for machine-to-machine communication by designing, implementing, and evaluating AGasP, an application for… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1806.00555v2-abstract-full').style.display = 'inline'; document.getElementById('1806.00555v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1806.00555v2-abstract-full" style="display: none;"> Blockchain technologies, such as smart contracts, present a unique interface for machine-to-machine communication that provides a secure, append-only record that can be shared without trust and without a central administrator. We study the possibilities and limitations of using smart contracts for machine-to-machine communication by designing, implementing, and evaluating AGasP, an application for automated gasoline purchases. We find that using smart contracts allows us to directly address the challenges of transparency, longevity, and trust in IoT applications. However, real-world applications using smart contracts must address their important trade-offs, such as performance, privacy, and the challenge of ensuring they are written correctly. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1806.00555v2-abstract-full').style.display = 'none'; document.getElementById('1806.00555v2-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 January, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 1 June, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1803.09835">arXiv:1803.09835</a> <span> [<a href="https://arxiv.org/pdf/1803.09835">pdf</a>, <a href="https://arxiv.org/format/1803.09835">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Databases">cs.DB</span> </div> </div> <p class="title is-5 mathjax"> Locality-Sensitive Hashing for Earthquake Detection: A Case Study of Scaling Data-Driven Science </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Rong%2C+K">Kexin Rong</a>, <a href="/search/cs?searchtype=author&query=Yoon%2C+C+E">Clara E. Yoon</a>, <a href="/search/cs?searchtype=author&query=Bergen%2C+K+J">Karianne J. Bergen</a>, <a href="/search/cs?searchtype=author&query=Elezabi%2C+H">Hashem Elezabi</a>, <a href="/search/cs?searchtype=author&query=Bailis%2C+P">Peter Bailis</a>, <a href="/search/cs?searchtype=author&query=Levis%2C+P">Philip Levis</a>, <a href="/search/cs?searchtype=author&query=Beroza%2C+G+C">Gregory C. Beroza</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="1803.09835v2-abstract-short" style="display: inline;"> In this work, we report on a novel application of Locality Sensitive Hashing (LSH) to seismic data at scale. Based on the high waveform similarity between reoccurring earthquakes, our application identifies potential earthquakes by searching for similar time series segments via LSH. However, a straightforward implementation of this LSH-enabled application has difficulty scaling beyond 3 months of… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1803.09835v2-abstract-full').style.display = 'inline'; document.getElementById('1803.09835v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1803.09835v2-abstract-full" style="display: none;"> In this work, we report on a novel application of Locality Sensitive Hashing (LSH) to seismic data at scale. Based on the high waveform similarity between reoccurring earthquakes, our application identifies potential earthquakes by searching for similar time series segments via LSH. However, a straightforward implementation of this LSH-enabled application has difficulty scaling beyond 3 months of continuous time series data measured at a single seismic station. As a case study of a data-driven science workflow, we illustrate how domain knowledge can be incorporated into the workload to improve both the efficiency and result quality. We describe several end-to-end optimizations of the analysis pipeline from pre-processing to post-processing, which allow the application to scale to time series data measured at multiple seismic stations. Our optimizations enable an over 100$\times$ speedup in the end-to-end analysis pipeline. This improved scalability enabled seismologists to perform seismic analysis on more than ten years of continuous time series data from over ten seismic stations, and has directly enabled the discovery of 597 new earthquakes near the Diablo Canyon nuclear power plant in California and 6123 new earthquakes in New Zealand. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1803.09835v2-abstract-full').style.display = 'none'; document.getElementById('1803.09835v2-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 July, 2018; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 26 March, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1705.01662">arXiv:1705.01662</a> <span> [<a href="https://arxiv.org/pdf/1705.01662">pdf</a>, <a href="https://arxiv.org/format/1705.01662">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"> Execution Templates: Caching Control Plane Decisions for Strong Scaling of Data Analytics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mashayekhi%2C+O">Omid Mashayekhi</a>, <a href="/search/cs?searchtype=author&query=Qu%2C+H">Hang Qu</a>, <a href="/search/cs?searchtype=author&query=Shah%2C+C">Chinmayee Shah</a>, <a href="/search/cs?searchtype=author&query=Levis%2C+P">Philip Levis</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="1705.01662v1-abstract-short" style="display: inline;"> Control planes of cloud frameworks trade off between scheduling granularity and performance. Centralized systems schedule at task granularity, but only schedule a few thousand tasks per second. Distributed systems schedule hundreds of thousands of tasks per second but changing the schedule is costly. We present execution templates, a control plane abstraction that can schedule hundreds of thousa… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1705.01662v1-abstract-full').style.display = 'inline'; document.getElementById('1705.01662v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1705.01662v1-abstract-full" style="display: none;"> Control planes of cloud frameworks trade off between scheduling granularity and performance. Centralized systems schedule at task granularity, but only schedule a few thousand tasks per second. Distributed systems schedule hundreds of thousands of tasks per second but changing the schedule is costly. We present execution templates, a control plane abstraction that can schedule hundreds of thousands of tasks per second while supporting fine-grained, per-task scheduling decisions. Execution templates leverage a program's repetitive control flow to cache blocks of frequently-executed tasks. Executing a task in a template requires sending a single message. Large-scale scheduling changes install new templates, while small changes apply edits to existing templates. Evaluations of execution templates in Nimbus, a data analytics framework, find that they provide the fine-grained scheduling flexibility of centralized control planes while matching the strong scaling of distributed ones. Execution templates support complex, real-world applications, such as a fluid simulation with a triply nested loop and data dependent branches. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1705.01662v1-abstract-full').style.display = 'none'; document.getElementById('1705.01662v1-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, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2017. </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 at USENIX ATC 2017</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1703.05028">arXiv:1703.05028</a> <span> [<a href="https://arxiv.org/pdf/1703.05028">pdf</a>, <a href="https://arxiv.org/format/1703.05028">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Databases">cs.DB</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/10.1145/3183713.3183729">10.1145/3183713.3183729 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Fonduer: Knowledge Base Construction from Richly Formatted Data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Wu%2C+S">Sen Wu</a>, <a href="/search/cs?searchtype=author&query=Hsiao%2C+L">Luke Hsiao</a>, <a href="/search/cs?searchtype=author&query=Cheng%2C+X">Xiao Cheng</a>, <a href="/search/cs?searchtype=author&query=Hancock%2C+B">Braden Hancock</a>, <a href="/search/cs?searchtype=author&query=Rekatsinas%2C+T">Theodoros Rekatsinas</a>, <a href="/search/cs?searchtype=author&query=Levis%2C+P">Philip Levis</a>, <a href="/search/cs?searchtype=author&query=R%C3%A9%2C+C">Christopher R茅</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="1703.05028v2-abstract-short" style="display: inline;"> We focus on knowledge base construction (KBC) from richly formatted data. In contrast to KBC from text or tabular data, KBC from richly formatted data aims to extract relations conveyed jointly via textual, structural, tabular, and visual expressions. We introduce Fonduer, a machine-learning-based KBC system for richly formatted data. Fonduer presents a new data model that accounts for three chall… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1703.05028v2-abstract-full').style.display = 'inline'; document.getElementById('1703.05028v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1703.05028v2-abstract-full" style="display: none;"> We focus on knowledge base construction (KBC) from richly formatted data. In contrast to KBC from text or tabular data, KBC from richly formatted data aims to extract relations conveyed jointly via textual, structural, tabular, and visual expressions. We introduce Fonduer, a machine-learning-based KBC system for richly formatted data. Fonduer presents a new data model that accounts for three challenging characteristics of richly formatted data: (1) prevalent document-level relations, (2) multimodality, and (3) data variety. Fonduer uses a new deep-learning model to automatically capture the representation (i.e., features) needed to learn how to extract relations from richly formatted data. Finally, Fonduer provides a new programming model that enables users to convert domain expertise, based on multiple modalities of information, to meaningful signals of supervision for training a KBC system. Fonduer-based KBC systems are in production for a range of use cases, including at a major online retailer. We compare Fonduer against state-of-the-art KBC approaches in four different domains. We show that Fonduer achieves an average improvement of 41 F1 points on the quality of the output knowledge base---and in some cases produces up to 1.87x the number of correct entries---compared to expert-curated public knowledge bases. We also conduct a user study to assess the usability of Fonduer's new programming model. We show that after using Fonduer for only 30 minutes, non-domain experts are able to design KBC systems that achieve on average 23 F1 points higher quality than traditional machine-learning-based KBC approaches. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1703.05028v2-abstract-full').style.display = 'none'; document.getElementById('1703.05028v2-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> 1 March, 2018; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 March, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> SIGMOD 2018 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1606.01972">arXiv:1606.01972</a> <span> [<a href="https://arxiv.org/pdf/1606.01972">pdf</a>, <a href="https://arxiv.org/format/1606.01972">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"> Scalable, Fast Cloud Computing with Execution Templates </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mashayekhi%2C+O">Omid Mashayekhi</a>, <a href="/search/cs?searchtype=author&query=Qu%2C+H">Hang Qu</a>, <a href="/search/cs?searchtype=author&query=Shah%2C+C">Chinmayee Shah</a>, <a href="/search/cs?searchtype=author&query=Levis%2C+P">Philip Levis</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="1606.01972v1-abstract-short" style="display: inline;"> Large scale cloud data analytics applications are often CPU bound. Most of these cycles are wasted: benchmarks written in C++ run 10-51 times faster than frameworks such as Naiad and Spark. However, calling faster implementations from those frameworks only sees moderate (3-5x) speedups because their control planes cannot schedule work fast enough. This paper presents execution templates, a contr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1606.01972v1-abstract-full').style.display = 'inline'; document.getElementById('1606.01972v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1606.01972v1-abstract-full" style="display: none;"> Large scale cloud data analytics applications are often CPU bound. Most of these cycles are wasted: benchmarks written in C++ run 10-51 times faster than frameworks such as Naiad and Spark. However, calling faster implementations from those frameworks only sees moderate (3-5x) speedups because their control planes cannot schedule work fast enough. This paper presents execution templates, a control plane abstraction for CPU-bound cloud applications, such as machine learning. Execution templates leverage highly repetitive control flow to cache scheduling decisions as {\it templates}. Rather than reschedule hundreds of thousands of tasks on every loop execution, nodes instantiate these templates. A controller's template specifies the execution across all worker nodes, which it partitions into per-worker templates. To ensure that templates execute correctly, controllers dynamically patch templates to match program control flow. We have implemented execution templates in Nimbus, a C++ cloud computing framework. Running in Nimbus, analytics benchmarks can run 16-43 times faster than in Naiad and Spark. Nimbus's control plane can scale out to run these faster benchmarks on up to 100 nodes (800 cores). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1606.01972v1-abstract-full').style.display = 'none'; document.getElementById('1606.01972v1-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> 6 June, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2016. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1606.01966">arXiv:1606.01966</a> <span> [<a href="https://arxiv.org/pdf/1606.01966">pdf</a>, <a href="https://arxiv.org/format/1606.01966">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"> Distributed Graphical Simulation in the Cloud </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mashayekhi%2C+O">Omid Mashayekhi</a>, <a href="/search/cs?searchtype=author&query=Shah%2C+C">Chinmayee Shah</a>, <a href="/search/cs?searchtype=author&query=Qu%2C+H">Hang Qu</a>, <a href="/search/cs?searchtype=author&query=Lim%2C+A">Andrew Lim</a>, <a href="/search/cs?searchtype=author&query=Levis%2C+P">Philip Levis</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="1606.01966v1-abstract-short" style="display: inline;"> Graphical simulations are a cornerstone of modern media and films. But existing software packages are designed to run on HPC nodes, and perform poorly in the computing cloud. These simulations have complex data access patterns over complex data structures, and mutate data arbitrarily, and so are a poor fit for existing cloud computing systems. We describe a software architecture for running graphi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1606.01966v1-abstract-full').style.display = 'inline'; document.getElementById('1606.01966v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1606.01966v1-abstract-full" style="display: none;"> Graphical simulations are a cornerstone of modern media and films. But existing software packages are designed to run on HPC nodes, and perform poorly in the computing cloud. These simulations have complex data access patterns over complex data structures, and mutate data arbitrarily, and so are a poor fit for existing cloud computing systems. We describe a software architecture for running graphical simulations in the cloud that decouples control logic, computations and data exchanges. This allows a central controller to balance load by redistributing computations, and recover from failures. Evaluations show that the architecture can run existing, state-of-the-art simulations in the presence of stragglers and failures, thereby enabling this large class of applications to use the computing cloud for the first time. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1606.01966v1-abstract-full').style.display = 'none'; document.getElementById('1606.01966v1-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> 6 June, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2016. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1602.01412">arXiv:1602.01412</a> <span>  </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"> Canary: A Scheduling Architecture for High Performance Cloud Computing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Qu%2C+H">Hang Qu</a>, <a href="/search/cs?searchtype=author&query=Mashayekhi%2C+O">Omid Mashayekhi</a>, <a href="/search/cs?searchtype=author&query=Terei%2C+D">David Terei</a>, <a href="/search/cs?searchtype=author&query=Levis%2C+P">Philip Levis</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="1602.01412v2-abstract-short" style="display: inline;"> We present Canary, a scheduling architecture that allows high performance analytics workloads to scale out to run on thousands of cores. Canary is motivated by the observation that a central scheduler is a bottleneck for high performance codes: a handful of multicore workers can execute tasks faster than a controller can schedule them. The key insight in Canary is to reverse the responsibilities… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1602.01412v2-abstract-full').style.display = 'inline'; document.getElementById('1602.01412v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1602.01412v2-abstract-full" style="display: none;"> We present Canary, a scheduling architecture that allows high performance analytics workloads to scale out to run on thousands of cores. Canary is motivated by the observation that a central scheduler is a bottleneck for high performance codes: a handful of multicore workers can execute tasks faster than a controller can schedule them. The key insight in Canary is to reverse the responsibilities between controllers and workers. Rather than dispatch tasks to workers, which then fetch data as necessary, in Canary the controller assigns data partitions to workers, which then spawn and schedule tasks locally. We evaluate three benchmark applications in Canary on up to 64 servers and 1,152 cores on Amazon EC2. Canary achieves up to 9-90X speedup over Spark and up to 4X speedup over GraphX, a highly optimized graph analytics engine. While current centralized schedulers can schedule 2,500 tasks/second, each Canary worker can schedule 136,000 tasks/second per core and experiments show this scales out linearly, with 64 workers scheduling over 120 million tasks per second, allowing Canary to support optimized jobs running on thousands of cores. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1602.01412v2-abstract-full').style.display = 'none'; document.getElementById('1602.01412v2-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 April, 2016; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 3 February, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2016. </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">We have some presentation issues with the paper</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1506.07577">arXiv:1506.07577</a> <span> [<a href="https://arxiv.org/pdf/1506.07577">pdf</a>, <a href="https://arxiv.org/format/1506.07577">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Graphics">cs.GR</span> </div> </div> <p class="title is-5 mathjax"> Ebb: A DSL for Physical Simulation on CPUs and GPUs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bernstein%2C+G+L">Gilbert Louis Bernstein</a>, <a href="/search/cs?searchtype=author&query=Shah%2C+C">Chinmayee Shah</a>, <a href="/search/cs?searchtype=author&query=Lemire%2C+C">Crystal Lemire</a>, <a href="/search/cs?searchtype=author&query=DeVito%2C+Z">Zachary DeVito</a>, <a href="/search/cs?searchtype=author&query=Fisher%2C+M">Matthew Fisher</a>, <a href="/search/cs?searchtype=author&query=Levis%2C+P">Philip Levis</a>, <a href="/search/cs?searchtype=author&query=Hanrahan%2C+P">Pat Hanrahan</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="1506.07577v3-abstract-short" style="display: inline;"> Designing programming environments for physical simulation is challenging because simulations rely on diverse algorithms and geometric domains. These challenges are compounded when we try to run efficiently on heterogeneous parallel architectures. We present Ebb, a domain-specific language (DSL) for simulation, that runs efficiently on both CPUs and GPUs. Unlike previous DSLs, Ebb uses a three-lay… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1506.07577v3-abstract-full').style.display = 'inline'; document.getElementById('1506.07577v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1506.07577v3-abstract-full" style="display: none;"> Designing programming environments for physical simulation is challenging because simulations rely on diverse algorithms and geometric domains. These challenges are compounded when we try to run efficiently on heterogeneous parallel architectures. We present Ebb, a domain-specific language (DSL) for simulation, that runs efficiently on both CPUs and GPUs. Unlike previous DSLs, Ebb uses a three-layer architecture to separate (1) simulation code, (2) definition of data structures for geometric domains, and (3) runtimes supporting parallel architectures. Different geometric domains are implemented as libraries that use a common, unified, relational data model. By structuring the simulation framework in this way, programmers implementing simulations can focus on the physics and algorithms for each simulation without worrying about their implementation on parallel computers. Because the geometric domain libraries are all implemented using a common runtime based on relations, new geometric domains can be added as needed, without specifying the details of memory management, mapping to different parallel architectures, or having to expand the runtime's interface. We evaluate Ebb by comparing it to several widely used simulations, demonstrating comparable performance to hand-written GPU code where available, and surpassing existing CPU performance optimizations by up to 9$\times$ when no GPU code exists. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1506.07577v3-abstract-full').style.display = 'none'; document.getElementById('1506.07577v3-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> 24 February, 2016; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 24 June, 2015; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2015. </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> 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