White Papers
.avif)
AI Platform and Data Infrastructure teams rely on Alluxio Data Acceleration Platform to boost the performance of data-intensive AI workloads, empower ML engineers to build models faster, and lower infrastructure costs.
With high-performance distributed cache architecture as its core, the Alluxio Data Acceleration Platform decouples storage capacity from storage performance, enabling you to more efficiently and cost-effectively grow storage capacity without worrying about performance.
- Data Acceleration
- Simplicity at Scale
- Architected for AI Workload Portability
- Lower Infrastructure Costs
In this datasheet, you will learn how Alluxio helps eliminate data loading bottlenecks and maximize GPU utilization for your AI workloads.
AI and machine learning workloads depend on accessing massive datasets to drive model development. However, when project teams attempt to transition pilots to production-level deployments, most discover their existing data architectures struggle to meet the performance demands.
This whitepaper discusses critical architectural considerations for optimizing data access and movement in enterprise-grade AI infrastructure. Discover:
- Common data access bottlenecks that throttle AI project productivity as workloads scale
- Why common approaches like faster storage and NAS/NFS fall short
- How Alluxio serves as a performant and scalable data access layer purpose-built for ML workloads
- Reference architecture on AWS and benchmarks test results
Explores the transformative capabilities of the Data Access Layer and how it can simplify and accelerate your analytics and AI workloads.
Kevin Petrie, VP of Research at Eckerson Group, shares the following insights in this new research paper:
- The elusive goal of analytics and AI performance
- The architecture of a Data Access Layer in the modern data stack
- The six use cases of the Data Access Layer, including analytics and AI in hybrid environments, workload bursts, cost optimization, migrations and more
- Guiding principles for making your data and AI projects successful
Kevin Petrie
VP of Research
.png)
Tachyon is a distributed file system enabling reliable data sharing at memory speed across cluster computing frameworks. While caching today improves read workloads, writes are either network or disk bound, as replication is used for fault-tolerance. Tachyon eliminates this bottleneck by pushing lineage, a well-known technique, into the storage layer. The key challenge in making a long-running lineagebased storage system is timely data recovery in case of failures. Tachyon addresses this issue by introducing a checkpointing algorithm that guarantees bounded recovery cost and resource allocation strategies for recomputation under commonly used resource schedulers. Our evaluation shows that Tachyon outperforms in-memory HDFS by 110x for writes. It also improves the end-to-end latency of a realistic workflow by 4x. Tachyon is open source and is deployed at multiple companies.
As ever more big data computations start to be in-memory, I/O throughput dominates the running times of many workloads. For distributed storage, the read throughput can be improved using caching, however, the write throughput is limited by both disk and network bandwidth due to data replication for fault-tolerance. This paper proposes a new file system architecture to enable frameworks to both read and write reliably at memory speed, by avoiding synchronous data replication on writes.