Electronic Arts (EA) is a leading company in the gaming industry, providing over a thousand games to serve billions of users worldwide. The EA Data & AI Department builds hundreds of platforms to manage petabytes of data generated by games and users every day. These platforms consist of a wide range of data analytics, from real-time data ingestion to ETL pipelines. Formatted data produced by our department is widely adopted by executives, producers, product managers, game engineers, and designers for marketing and monetization, game design, customer engagement, player retention, and end-user experience.
Near real-time information for EA’s online services is critical for making business decisions, such as campaigns and troubleshooting. These services include, but are not limited to, real-time data visualization, dashboarding, and conversational analytics. Highly time-sensitive applications such as BI software, dashboards and AI tools heavily rely on these services. To support these use cases, we studied an innovative platform with Presto as the computing engine and Alluxio as a data orchestration layer between Presto and S3 storage. We evaluated this platform with real industrial examples of data visualization, dashboarding, and a conversational chatbot. Our preliminary results show that Presto with Alluxio outperforms S3 significantly in all cases, with a 6x performance gain when handling a large number of small files.
Electronic Arts (EA) is a leading company in the gaming industry, providing over a thousand games to serve billions of users worldwide. The EA Data & AI Department builds hundreds of platforms to manage petabytes of data generated by games and users every day. These platforms consist of a wide range of data analytics, from real-time data ingestion to ETL pipelines. Formatted data produced by our department is widely adopted by executives, producers, product managers, game engineers, and designers for marketing and monetization, game design, customer engagement, player retention, and end-user experience.
Near real-time information for EA’s online services is critical for making business decisions, such as campaigns and troubleshooting. These services include, but are not limited to, real-time data visualization, dashboarding, and conversational analytics. Highly time-sensitive applications such as BI software, dashboards and AI tools heavily rely on these services. To support these use cases, we studied an innovative platform with Presto as the computing engine and Alluxio as a data orchestration layer between Presto and S3 storage. We evaluated this platform with real industrial examples of data visualization, dashboarding, and a conversational chatbot. Our preliminary results show that Presto with Alluxio outperforms S3 significantly in all cases, with a 6x performance gain when handling a large number of small files.
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Electronic Arts (EA) is a leading company in the gaming industry, providing over a thousand games to serve billions of users worldwide. The EA Data & AI Department builds hundreds of platforms to manage petabytes of data generated by games and users every day. These platforms consist of a wide range of data analytics, from real-time data ingestion to ETL pipelines. Formatted data produced by our department is widely adopted by executives, producers, product managers, game engineers, and designers for marketing and monetization, game design, customer engagement, player retention, and end-user experience.
Near real-time information for EA’s online services is critical for making business decisions, such as campaigns and troubleshooting. These services include, but are not limited to, real-time data visualization, dashboarding, and conversational analytics. Highly time-sensitive applications such as BI software, dashboards and AI tools heavily rely on these services. To support these use cases, we studied an innovative platform with Presto as the computing engine and Alluxio as a data orchestration layer between Presto and S3 storage. We evaluated this platform with real industrial examples of data visualization, dashboarding, and a conversational chatbot. Our preliminary results show that Presto with Alluxio outperforms S3 significantly in all cases, with a 6x performance gain when handling a large number of small files.
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In the rapidly evolving landscape of AI and machine learning, Platform and Data Infrastructure Teams face critical challenges in building and managing large-scale AI platforms. Performance bottlenecks, scalability of the platform, and scarcity of GPUs pose significant challenges in supporting large-scale model training and serving.
In this talk, we introduce how Alluxio helps Platform and Data Infrastructure teams deliver faster, more scalable platforms to ML Engineering teams developing and training AI models. Alluxio’s highly-distributed cache accelerates AI workloads by eliminating data loading bottlenecks and maximizing GPU utilization. Customers report up to 4x faster training performance with high-speed access to petabytes of data spread across billions of files regardless of persistent storage type or proximity to GPU clusters. Alluxio’s architecture lowers data infrastructure costs, increases GPU utilization, and enables workload portability for navigating GPU scarcity challenges.
TorchTitan is a proof-of-concept for Large-scale LLM training using native PyTorch. It is a repo that showcases PyTorch's latest distributed training features in a clean, minimal codebase.
In this talk, Tianyu will share TorchTitan’s design and optimizations for the Llama 3.1 family of LLMs, spanning 8 billion to 405 billion parameters, and showcase its performance, composability, and scalability.