Distributed Systems and Parallel Computing

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</div> </div> <div class="glue-header__drawer-backdrop"> <div class="glue-header__mobile_close"> <button class="glue-header__drawer-toggle-btn js-mobile-nav-close" aria-label="Close the navigation drawer"> <svg class="glue-icon glue-icon--24px" role="presentation" aria-hidden="true"> <use href="/gr/static/assets/icons/glue-icons.svg#close"></use> </svg> </button> </div> </div> </header> <main id="page-content"> <div class="research-area-detail"> <section class="basic-hero bhoig --theme-dark " data-gt-id="basic_hero" data-gt-component-name=""> <div class="glue-page"> <div class="glue-grid"> <div class="bhoig__image-wrapper glue-grid__col--span-2 glue-grid__col--span-3-md glue-grid__col--span-2-lg"> <div class="bhoig__image-bg" style="background-color: var(--glue-grey-900); "> <span class="icon icon--distributed-systems-and-parallel-computing" style="" ></span> </div> </div> <div class="bhoig__breadcrumb-wrapper glue-grid__col--span-10 glue-grid__col--span-9-md glue-grid__col--span-10-lg"> <nav class="glue-breadcrumbs" aria-label="Breadcrumbs"> <ol class="glue-breadcrumbs__list"> <li class="glue-breadcrumbs__item"> <a class="glue-breadcrumbs__link attribution" href="/">Home</a> <svg role="presentation" aria-hidden="true" class="glue-icon "> <use href="/gr/static/assets/icons/glue-icons.svg#chevron-right"></use> </svg> </li> <li class="glue-breadcrumbs__item"> <a class="glue-breadcrumbs__link attribution" href="/research-areas/">Research areas</a> <svg role="presentation" aria-hidden="true" class="glue-icon "> <use href="/gr/static/assets/icons/glue-icons.svg#chevron-right"></use> </svg> </li> </ol> </nav> </div> <h1 class="headline-1 bhoig__headline glue-grid__col--span-10 glue-grid__col--span-9-md glue-grid__col--span-10-lg">Distributed Systems and Parallel Computing</h1> <div class="basic-hero__description bhoig__description glue-grid__col--span-10 glue-grid__col--span-9-md glue-grid__col--span-10-lg"> <p data-block-key="dhqf9">No matter how powerful individual computers become, there are still reasons to harness the power of multiple computational units, often spread across large geographic areas. Sometimes this is motivated by the need to collect data from widely dispersed locations (e.g., web pages from servers, or sensors for weather or traffic). Other times it is motivated by the need to perform enormous computations that simply cannot be done by a single CPU.</p><p data-block-key="l254"></p><p data-block-key="3791p">From our company’s beginning, Google has had to deal with both issues in our pursuit of organizing the world’s information and making it universally accessible and useful. We continue to face many exciting distributed systems and parallel computing challenges in areas such as concurrency control, fault tolerance, algorithmic efficiency, and communication. Some of our research involves answering fundamental theoretical questions, while other researchers and engineers are engaged in the construction of systems to operate at the largest possible scale, thanks to our <a href="https://cacm.acm.org/magazines/2012/7/151226-googles-hybrid-approach-to-research/fulltext" target="_blank" rel="noopener noreferrer">hybrid research model</a>.</p> </div> <div class="bhoig__cta glue-grid__col--span-10 glue-grid__col--span-9-md glue-grid__col--span-10-lg"> </div> </div> </div> </section> <section class="offset-two-up"> <div class="glue-page glue-grid"> <div class="offset-two-up__left-col glue-grid__col glue-grid__col--span-4-sm glue-grid__col--span-12-md glue-grid__col--span-3-lg"> <h3 class="offset-two-up__headline headline-3">Recent Publications</h3> <div class="offset-two-up__cta-below-description"> <a class="glue-button glue-button--medium-emphasis" href="http://research.google/pubs/?category=distributed-systems-and-parallel-computing" > <span class="js-gt-item-id">See More</span> </a> </div> </div> <div class="glue-grid__col glue-grid__col--span-4-sm glue-grid__col--span-12-md glue-grid__col--span-9-lg"> <div class="publications-list --theme- row-card-list" data-hot-swap="pub-list" data-gt-id="publications_list" data-gt-component-name=""> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/how-we-use-genai-in-sre/ > How we use GenAI in SRE </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/ramonmedranollamas/"> Ramón Medrano Llamas </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> CommitConf, Madrid (2024) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentgoogle-services-are-powered-by-the tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentgoogle-services-are-powered-by-the" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">Google services are powered by the largest network of computers in the world. Site Reliabity Engineers (SRE) make sure that the whole stack is cool: datacenters are safe, well provisionedl; we have fallback mechanims, and data integrity; to making sure we design our stack properly, using the right storage, replication and software trade-offs. Generative AI is a great tool to make us super-effective: having access to tools to generate our most toily configurations, to classify risks and events, to manage large swaths of machines with agents or to automate complex workflows cheaply. This talk will cover the journey that SRE started years ago to become a truly AI-First discipline and the latest advancements in tooling, practices and workflows.</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/how-we-use-genai-in-sre/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/load-is-not-what-you-should-balance-introducing-prequal/ > Load is not what you should balance: Introducing Prequal </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> Bartek Wydrowski </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Bobby Kleinberg </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Steve Rumble </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/aaronarcher/"> Aaron Archer </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> (2024) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentwe-present-prequal-emphprobing tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentwe-present-prequal-emphprobing" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">We present Prequal (\emph{Probing to Reduce Queuing and Latency}), a load balancer for distributed multi-tenant systems. Prequal aims to minimize real-time request latency in the presence of heterogeneous server capacities and non-uniform, time-varying antagonist load. It actively probes server load to leverage the \emph{power of $d$ choices} paradigm, extending it with asynchronous and reusable probes. Cutting against received wisdom, Prequal does not balance CPU load, but instead selects servers according to estimated latency and active requests-in-flight (RIF). We explore its major design features on a testbed system and evaluate it on YouTube, where it has been deployed for more than two years. Prequal has dramatically decreased tail latency, error rates, and resource use, enabling YouTube and other production systems at Google to run at much higher utilization.</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/load-is-not-what-you-should-balance-introducing-prequal/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/federated-variational-inference-towards-improved-personalization-and-generalization/ > Federated Variational Inference: Towards Improved Personalization and Generalization </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> Elahe Vedadi </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Josh Dillon </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Philip Mansfield </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Karan Singhal </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Arash Afkanpour </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Warren Morningstar </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> AAAI Federated Learning on the Edge Symposium (2024) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentconventional-federated-learning-al tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentconventional-federated-learning-al" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">Conventional federated learning algorithms train a single global model by leveraging all participating clients' data. However, due to heterogeneity in client generative distributions and predictive models, these approaches may not appropriately approximate the predictive process, converge to an optimal state, or generalize to new clients. We study personalization and generalization in stateless cross-device federated learning setups assuming heterogeneity in client data distributions and predictive models. We first propose a hierarchical generative model and formalize it using Bayesian Inference. We then approximate this process using Variational Inference to train our model efficiently. We call this algorithm Federated Variational Inference (FedVI). We use PAC-Bayes analysis to provide generalization bounds for FedVI. We evaluate our model on FEMNIST and CIFAR-100 image classification and show that FedVI beats the state-of-the-art on both tasks.</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/federated-variational-inference-towards-improved-personalization-and-generalization/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/vortex-a-stream-oriented-storage-engine-for-big-data-analytics/ > Vortex: A Stream-oriented Storage Engine For Big Data Analytics </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/pavanedara/"> Pavan Edara </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Jonathan Forbes </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Bigang Li </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> SIGMOD (2024) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentvortex-is-an-exabyte-scale-structu tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentvortex-is-an-exabyte-scale-structu" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">Vortex is an exabyte scale structured storage system built for streaming and batch analytics. It supports high-throughput batch and stream ingestion. For the user, it supports both batch-oriented and stream-based processing on the ingested data.</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/vortex-a-stream-oriented-storage-engine-for-big-data-analytics/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/thesios-synthesizing-accurate-counterfactual-io-traces-from-io-samples/ > Thesios: Synthesizing Accurate Counterfactual I/O Traces from I/O Samples </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> Mangpo Phothilimthana </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/saurabhkadekodi/"> Saurabh Kadekodi </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Soroush Ghodrati </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Selene Moon </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/martinmaas/"> Martin Maas </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> ASPLOS 2024, Association for Computing Machinery </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentrepresentative-modeling-of-io-act tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentrepresentative-modeling-of-io-act" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">Representative modeling of I/O activity is crucial when designing large-scale distributed storage systems. Particularly important use cases are counterfactual “what-if” analyses that assess the impact of anticipated or hypothetical new storage policies or hardware prior to deployment. We propose Thesios, a methodology to accurately synthesize such hypothetical full-resolution I/O traces by carefully combining down-sampled I/O traces collected from multiple disks attached to multiple storage servers. Applying this approach to real-world traces that a real ready routinely sampled at Google, we show that our synthesized traces achieve 95–99.5% accuracy in read/write request numbers, 90–97% accuracy in utilization, and 80–99.8% accuracy in read latency compared to metrics collected from actual disks. We demonstrate how The-sios enables diverse counterfactual I/O trace synthesis and analyses of hypothetical policy, hardware, and server changes through four case studies: (1) studying the effects of changing disk’s utilization, fullness, and capacity, (2) evaluating new data placement policy, (3) analyzing the impact on power and performance of deploying disks with reduced rotations-per-minute (RPM), and (4) understanding the impact of increased buffer cache size on a storage server. Without Thesios, such counterfactual analyses would require costly and potentially risky A/B experiments in production.</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/thesios-synthesizing-accurate-counterfactual-io-traces-from-io-samples/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/biglake-bigquerys-evolution-toward-a-multi-cloud-lakehouse/ > BigLake: BigQuery’s Evolution toward a Multi-Cloud Lakehouse </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> Justin Levandoski </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Garrett Casto </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Mingge Deng </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Rushabh Desai </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/pavanedara/"> Pavan Edara </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Thibaud Hottelier </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Amir Hormati </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/anoopjohnson/"> Anoop Johnson </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Jeff Johnson </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Dawid Kurzyniec </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/sammcveety/"> Sam McVeety </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Prem Ramanathan </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Gaurav Saxena </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Vidya Shanmugam </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Yuri Volobuev </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> SIGMOD (2024) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentbigquerys-cloud-native-disaggrega tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentbigquerys-cloud-native-disaggrega" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">BigQuery’s cloud-native disaggregated architecture has allowed Google Cloud to evolve the system to meet several customer needs across the analytics and AI/ML workload spectrum. A key customer requirement for BigQuery centers around the unification of data lake and enterprise data warehousing workloads. This approach combines: (1) the need for core data management primitives, e.g., security, governance, common runtime metadata, performance acceleration, ACID transactions, provided by an enterprise data warehouses coupled with (2) harnessing the flexibility of the open source format and analytics ecosystem along with new workload types such as AI/ML over unstructured data on object storage. In addition, there is a strong requirement to support BigQuery as a multi-cloud offering given cloud customers are opting for a multi-cloud footprint by default. This paper describes BigLake, an evolution of BigQuery toward a multi-cloud lakehouse to address these customer requirements in novel ways. We describe three main innovations in this space. We first present BigLake tables, making open-source table formats (e.g., Apache Parquet, Iceberg) first class citizens, providing fine-grained governance enforcement and performance acceleration over these formats to BigQuery and other open-source analytics engines. Next, we cover the design and implementation of BigLake Object tables that allow BigQuery to integrate AI/ML for inferencing and processing over unstructured data. Finally, we present Omni, a platform for deploying BigQuery on non-GCP clouds, focusing on the infrastructure and operational innovations we made to provide an enterprise lakehouse product regardless of the cloud provider hosting the data.</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/biglake-bigquerys-evolution-toward-a-multi-cloud-lakehouse/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> </div> </div> </div> </section> <section class="offset-two-up --theme-dark both" data-gt-id="offset_two_up" data-gt-component-name=""> <div class="glue-page glue-grid"> <div class="offset-two-up__left-col glue-grid__col glue-grid__col--span-4-sm glue-grid__col--span-12-md glue-grid__col--span-3-lg"> <h2 class="offset-two-up__headline headline-3">Some of our teams</h2> </div> <div class="glue-grid__col glue-grid__col--span-4-sm glue-grid__col--span-12-md glue-grid__col--span-9-lg"> <ul class="card-stack--small nested-glue-grid-override" data-gt-id="team_small_card_stack" data-gt-component-name="Team - Small Card Stack"> <li class="glue-grid__col glue-grid__col--span-6-md glue-grid__col--span-4-sm"> <a class="glue-card not-glue glue-card--small" href="/teams/algorithms-optimization/"> <div class="glue-card__inner"> <div class="glue-card__content"> <div class="content-wrapper"> <p class="glue-headline body js-gt-item-id"> Algorithms & optimization </p> </div> <div class="glue-card__cta glue-card__cta--arrow"> <span class="glue-button glue-button--low-emphasis glue-button--icon"> <svg role="presentation" aria-hidden="true" class="glue-icon "> <use href="/gr/static/assets/icons/glue-icons.svg#arrow-forward"></use> </svg> </span> </div> </div> </div> </a> </li> <li class="glue-grid__col glue-grid__col--span-6-md glue-grid__col--span-4-sm"> <a class="glue-card not-glue glue-card--small" href="/teams/athena/"> <div class="glue-card__inner"> <div class="glue-card__content"> <div class="content-wrapper"> <p class="glue-headline body js-gt-item-id"> Athena </p> </div> <div class="glue-card__cta glue-card__cta--arrow"> <span class="glue-button glue-button--low-emphasis glue-button--icon"> <svg role="presentation" aria-hidden="true" class="glue-icon "> <use href="/gr/static/assets/icons/glue-icons.svg#arrow-forward"></use> </svg> </span> </div> </div> </div> </a> </li> <li class="glue-grid__col glue-grid__col--span-6-md glue-grid__col--span-4-sm"> <a class="glue-card not-glue glue-card--small" href="/teams/graph-mining/"> <div class="glue-card__inner"> <div class="glue-card__content"> <div class="content-wrapper"> <p class="glue-headline body js-gt-item-id"> Graph mining </p> </div> <div class="glue-card__cta glue-card__cta--arrow"> <span class="glue-button glue-button--low-emphasis glue-button--icon"> <svg role="presentation" aria-hidden="true" class="glue-icon "> <use href="/gr/static/assets/icons/glue-icons.svg#arrow-forward"></use> </svg> </span> </div> 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</div> <div class="glue-card__cta glue-card__cta--arrow"> <span class="glue-button glue-button--low-emphasis glue-button--icon"> <svg role="presentation" aria-hidden="true" class="glue-icon "> <use href="/gr/static/assets/icons/glue-icons.svg#arrow-forward"></use> </svg> </span> </div> </div> </div> </a> </li> </ul> </div> </div> </section> <section class="banner --theme-light" data-gt-id="banner" data-gt-component-name="None"> <div class="banner__wrapper glue-page glue-grid"> <div class="banner__copy glue-grid__col glue-grid__col--span-4-sm glue-grid__col--span-6-md"> <h2 class="banner__headline headline-3">Join us</h2> <p class="banner__body-copy body">We're always looking for more talented, passionate people.</p> <a class="glue-button glue-button--medium-emphasis" href="http://research.google/careers/" > <span class="js-gt-item-id">See opportunities</span> </a> </div> <div class="glue-grid__col glue-grid__col--span-0-sm glue-grid__col--span-1"></div> <div class="banner__image 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