Alluxio Webinar | Optimize, Don't Overspend: Data Caching Strategy for AI Workloads
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The document details Alluxio's data caching strategy designed to optimize AI workloads while minimizing costs and enhancing performance. Key features include improved GPU utilization, high-performance caching, and support for various storage systems, showcasing significant performance advantages over competitors in bandwidth and scalability. It highlights case studies, user benefits, and encourages engagement through a quick deployment trial of Alluxio's services.
Alluxio Webinar | Optimize, Don't Overspend: Data Caching Strategy for AI Workloads
- 1. Optimize, Don't Overspend: Data Caching Strategy for AI Workloads Sep, 2024
- 2. Alluxio makes it easy to share and manage data from any storage to any compute engine in any environment, with high performance and low cost. 2
- 3. 3 Open Source Started From UC Berkeley AMPLab in 2014 JOIN THE CONVERSATION ON SLACK ALLUXIO.IO/SLACK 1,200+ contributors & growing 10,000+ Slack Community Members Top 10 Most Critical Java Based Open Source Project Top 100 Most Valuable Repositories Out of 96 Million on GitHub
- 4. 4 Case Studies Zhihu TELCO & MEDIA E-COMMERCE FINANCIAL SERVICES TECH & INTERNET OTHERS
- 5. Leverage GPUs Anywhere Run AI workloads wherever GPUs are available without data locality concerns
- 7. 7 Critical infrastructure barriers to effective AI/ML adoption LOW PERFORMANCE COST MANAGEMENT High performance caching for model training & distribution GPU SCARCITY Multi-region/cloud data serving capability Shorten time-to-production $ $ Higher GPU Utilization Avoid copying across data lakes Utilize NVMe directly on the GPU cluster
- 8. I/O Performance for AI Training and GPU Utilization 1 HPC Performance on Existing Data Lakes Achieve up to 8GB/s throughput & 200K IOPS for a single client Improvements compared to 2.x: 35% for hot sequential reads, 20x for hot random reads, 4x for cold reads 2 GPU Saturation Fully saturate 8 A100 GPUs, showing over 97% GPU utilization in MLPerf Storage language processing benchmarks. Customer production data show GPU utilization improvement from 40% to 60% for search/recommendation models & 50% to 95% for LLMs 3 Checkpoint Optimization New checkpoint read/write support optimizes training with write caching capabilities
- 9. ● Alluxio 3.2: Achieved a bandwidth of 2081 MiB/s(1 thread) to 8183 MiB/s(32 threads) with a single client, significantly outperforming competitors. ● JuiceFS: Recorded a bandwidth of 1886 MiB/s(1 thread) to 6207 MiB/s, showing 9.3% to 24.1% slower than Alluxio 3.2. ● FSx Lustre: Managed a bandwidth of 185 MiB/s(1 thread) to 3992 MiB/s, showing 91.1% to 51.2% slower than Alluxio 3.2. ● Observations: Alluxio 3.2 shows better performance, particularly in handling sequential read operations efficiently. Comparison against other vendors | FIO - Sequential Read Setup ● Alluxio: 1 Alluxio worker (i3en.metal) 1 Alluxio fuse client (c5n.metal) ● AWS FSx Lustre (12TB capacity) ● JuiceFS (SaaS) Note: the Alluxio fuse client co-located with training servers is responsible for POSIX API access to Alluxio Workers which actually cache the data Alluxio Proprietary and Confidential
- 10. Comparison against other vendors | MLPerf Storage Setup ● Alluxio 1 fuse (c6in.metal) 2 worker (i3en.metal) Note: DDN with 12 GPUs and Weka with 20 GPUs are the available data points published on MLPerf website. Alluxio Proprietary and Confidential
- 12. Scalability Master as the bottleneck Unlimited scalability Support tens of billions of small files with single Alluxio cluster Reliability Fault tolerance Automatic Fallback to under file system More friendly to Kubernetes and Cloud Performance Zero-copy network transmission with netty High concurrent read Data Governance Multi-tenant & quota management Plugable security management Decentralized Object Repository Architecture (DORA) Motivation & Benefits
- 13. Architecture 70 70 AI/Analytics Applications Get Task Info Send Result Alluxio Client 13 Affinity Block Location Policy Client Consistent Hash (Task Info) 2 3 Service Registry Alluxio Worker Alluxio Worker Alluxio Worker Execute Task Get Cluster Info Find Worker(s) 1 4 Cache miss Under storage task 5 Training Node Alluxio Cluster Under Storage
- 14. Read Optimization High Concurrent Position Read Solve up to 150X Read Amplification issue Improve unstructured file parallel read up to 9X Improve structured file position read 2 - 15X Zero-copy Data Transmission Improve memory efficiency Improve large file sequential streaming read performance by 30% - 50%
- 15. 15 Example Use Case $
- 16. 16 BUSINESS BENEFIT: TECH BENEFIT: Increase GPU utilization 50% 93% HDFS Training Data Training Data M o d e l s Training Data Models Model Training Model Training Model Deployment Model Inference Downstream Applications Model Update Training Clouds Offline Cloud Online Cloud Zhihu CASE STUDY: High Performance AI Platform for LLM 2 - 4X faster time-to-market
- 17. 17 $ Try Alluxio For Free in 30 min!
- 18. Try the fully deployed Alluxio AI cluster for FREE! ● Explore the potential performance benefits of Alluxio by running FIO benchmarks ● Simplify the deployment process with preconfigured template clusters ● Maintain full control of your data with Alluxio deployed within your AWS account ● User friendly webUI with just a few clicks in under 40 minutes Blog with sign up link and tutorial Introducing Rapid Alluxio Deployer (RAD) in AWS! Example
- 19. 19 Thank you! $ Join the conversation on Slack alluxio.io/slack Sign up RAD at https://signup.alluxio-rad.io/ and send us a screenshot of the cluster you created to get a chance to win a $50 Amazon gift card!