Distributed Systems and Parallel Computing
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.
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 hybrid research model.
Recent Publications
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This talk addresses the challenges of operating Google's monitoring systems at scale, handling terabytes of telemetry data and preventing overload from diverse workloads. We'll explore how Google's internal client library and Monarch, its planet-scale time-series database, work together for cost-effective data collection. Key principles include a distributed push model, dynamic client-side data reduction, centralized retention, and periodic metric analysis. The session will then bridge these concepts to the open-source world, discussing our work with OpenTelemetry's OpAMP protocol to achieve similar scalable and efficient telemetry collection. Attendees will gain insights into adapting these principles for cost savings and learn about our collaboration with the OpAMP SIG to benefit the broader community.
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Optimizing large language model (LLM) training on dritibuted domain-specific accelerator systems presents significant challenges due to its complex optimization space and reliance on manual processes, resulting in slow development and underutilize resources. Existing optimization methods, however, rely on time-consuming manual tuning or resource-intensive black-box searches, which struggle to keep pace with the rapidly evolving LLM domain. To address this, we introduce the ASAP, an Agentic Solution to Auto-optimize Performance of Large-Scale LLM Training. It is a multi-agent system, featuring Coordinator, Analyzer, and Proposal agents, which integrates LLM reasoning with insights from performance profiling tools, analytical roofline models, and a knowledge base of best practices and successful past optimizations. Our proposed design can automate the diagnosis of performance bottlenecks and intelligently generates optimized sharding configurations with reasoning, and effectively improve the distributed LLM training efficiency. This approach promises to significantly reduce manual effort, shorten iteration cycles, and enhance accelerator utilization, offering a scalable and explanable methodology for AI-assisted performance engineering in large-scale machine learning.
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Continuous Evaluation: Using CI Techniques For Experimentation At Scale
IEEE (2025), pp. 157-157
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Continuous Integration (CI) is an essential software development practice that establishes processes to minimize bugs and errors in production. In a similar vein, experimentation of software products is vital for evaluating user satisfaction, quality, performance and other key business metrics. Experimentation allows product owners to evaluate the user impact of changes. This can help make informed decisions regarding feature launches. Experimentation also allows developers to tweak internal processes and algorithms to maximize the impact of new features and changes. Additionally, it can sometimes detect errors not detected by CI.
Unlike CI systems, experimentation platforms are meant to closely imitate production and usually run the system under test (SUT) against a large scale of input. Despite this, experimentation platforms have a lot in common with CI systems. The mechanisms for continuously integrating and testing changes can be modified and applied to experimentation platforms.
Google Search's experimentation platform started as a command line tool many years ago. Over time, this tool has evolved into a platform that serves the evaluation needs for many of Google's products like Search, Assistant, YouTube, Play, Lens, etc., running thousands of large experiments every day.
In this workshop, we will present the evolution of Google Search's experimentation platform and how it was transformed from a simple CLI tool into a platform that works at scale, fulfills continuous experimentation needs and provides many CI-like functionalities to its users.
Note: This presentation was a part of CCIW workshop a ISCT 2025. Please download slides to see full presentation.
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Fast ACS: Low-Latency File-Based Ordered Message Delivery at Scale
Anil Raghunath Iyer
Neel Bagora
Chang Yu
Olivier Pomerleau
Vivek Kumar
Prunthaban Kanthakumar
Usenix Annual Technical Conference (2025)
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Low-latency message delivery is crucial for real-time systems. Data originating from a producer must be delivered to consumers, potentially distributed in clusters across metropolitan and continental boundaries. With the growing scale of computing, there can be several thousand consumers of the data. Such systems require a robust messaging system capable of transmitting messages containing data across clusters and efficiently delivering them to consumers. The system must offer guarantees like ordering and at-least-once delivery while avoiding overload on consumers, allowing them to consume messages at their own pace.
This paper presents the design of Fast ACS (an abbreviation for Ads Copy Service), a file-based ordered message delivery system that leverages a combination of two-sided (inter-cluster) and one-sided (intra-cluster) communication primitives—namely, Remote Procedure Call and Remote Direct Memory Access, respectively—to deliver messages. The system has been successfully deployed to dozens of production clusters and scales to accommodate several thousand consumers within each cluster, which amounts to Tbps-scale intra-cluster consumer traffic at peak. Notably, Fast ACS delivers messages to consumers across the globe within a few seconds or even sub-seconds (p99) based on the message volume and consumer scale, at a low resource cost.
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The ASPLOS 2025 / EuroSys 2025 Contest on Intra-Operator Parallelism for Distributed Deep Learning
Pratik Fegade
Proceedings of the 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (2025), pp. 5-17
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A chief enabler of large-scale deep learning is the distribution of computation across multiple interconnected hardware accelerators. In order to unlock the maximum possible performance, a compiler must first select a reasonable strategy to parallelize a model's operations. Since neural network architectures admit multiple flavors of parallelism, determining the proper strategy for each instruction is a critical (albeit non-trivial) task. To solicit new ideas toward solving this challenging combinatorial optimization problem, we organized the ASPLOS 2025 / EuroSys 2025 Contest on Intra-Operator Parallelism for Distributed Deep Learning, a multi-month competition focused on advancing the state-of-the-art for model partitioning algorithms. In this paper, we offer a retrospective of this event, including the basic problem formulation, key challenges & opportunities, our new benchmark suite, and the quality of submissions received.
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How we use GenAI in SRE
CommitConf, Madrid (2024)
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Google services are powered by the largest network of computers in the world. Site Reliability Engineers (SRE) make sure that the whole stack is cool: datacenters are safe, well provisioned; we have fallback mechanisms, and data integrity; to make 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.
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