RAPIDS is a set of open source libraries enabling GPU aware scheduling and memory representation for analytics and AI. Spark 3.0 uses RAPIDS for GPU computing to accelerate various jobs including SQL and DataFrame. With compute acceleration from massive parallelism on GPUs, there is a need for accelerating data access and this is what Alluxio enables for compute in any cloud. In this talk, you will learn how to use Alluxio and Spark with RAPIDS Accelerator on NVIDIA GPUs without any application changes.
ALLUXIO DAY III 2021
April 27, 2021
RAPIDS is a set of open source libraries enabling GPU aware scheduling and memory representation for analytics and AI. Spark 3.0 uses RAPIDS for GPU computing to accelerate various jobs including SQL and DataFrame. With compute acceleration from massive parallelism on GPUs, there is a need for accelerating data access and this is what Alluxio enables for compute in any cloud. In this talk, you will learn how to use Alluxio and Spark with RAPIDS Accelerator on NVIDIA GPUs without any application changes.
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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.
As large-scale machine learning becomes increasingly GPU-centric, modern high-performance hardware like NVMe storage and RDMA networks (InfiniBand or specialized NICs) are becoming more widespread. To fully leverage these resources, it’s crucial to build a balanced architecture that avoids GPU underutilization. In this talk, we will explore various strategies to address this challenge by effectively utilizing these advanced hardware components. Specifically, we will present experimental results from building a Kubernetes-native distributed caching layer, utilizing NVMe storage and high-speed RDMA networks to optimize data access for PyTorch training.