Parallelstore
High performance, managed parallel file service
Parallelstore is based on Intel DAOS and delivers up to 6.3x greater read throughput performance compared to competitive Lustre scratch offerings.
Features
Fast, scalable performance
High bandwidth, high IOPS, and ultra-low latency, utilizing byte-addressable media to store metadata and small I/O, and locally attached NVMe with software-managed redundancy for bulk I/O.
Accelerate time to value
Configure Parallelstore to ideally fit your use case and build a system of the right scale for extreme generative AI and HPC simulation use cases.
Futureproof your architecture
Building with Parallelstore prepares your business for HPC scale, AI/ML convergence, and Kubernetes integration, which means you can grow and scale with minimal disruption. Distributed metadata management, extreme IOPS, and key value store architecture are well-aligned to emerging patterns in AI workloads.
Open source flexibility
Parallelstore is built on Intel DAOS, which is open source. We have a long history of leadership in open source—from projects like Kubernetes, TensorFlow, and more. Open source gives you the flexibility to deploy—and, if necessary, migrate—critical workloads across or off public cloud platforms. Bring products and services to market faster, without operational overhead or requirement of specialized skills.
How It Works
"With DAOS on Google Cloud, users can easily and quickly provision storage clusters that can scale to similar performance levels as similar on-premises hardware, but then also be able to dynamically grow and shrink those clusters as needed." - Andrey Kudryavtsev, Intel
Common Uses
AI/ML training
For systems that need high-speed access to very large files or millions of small files, parallel file systems can outperform NFS and object storage. The latency offered by parallel file systems is significantly lower than other options, which can affect the maximum IOPS, making Parallelstore a great choice for AI/ML workload scratch space.
Learning resources
For systems that need high-speed access to very large files or millions of small files, parallel file systems can outperform NFS and object storage. The latency offered by parallel file systems is significantly lower than other options, which can affect the maximum IOPS, making Parallelstore a great choice for AI/ML workload scratch space.
Quantitative trading analysis
Parallelstore is ideally suited to provide scratch analysis of intermediate data in high performance quantitative analysis and trading, and is also well-suited to other complex, high-speed financial services use cases such as fraud detection.
Learning resources
Parallelstore is ideally suited to provide scratch analysis of intermediate data in high performance quantitative analysis and trading, and is also well-suited to other complex, high-speed financial services use cases such as fraud detection.
Computer-Aided Engineering (CAE)
Parallelstore is a good option for manufacturing, auto, aerospace, and biomedical applications doing computational fluid dynamics (CFD), complex modeling, crash simulation, chemical engineering, and more.
Learning resources
Parallelstore is a good option for manufacturing, auto, aerospace, and biomedical applications doing computational fluid dynamics (CFD), complex modeling, crash simulation, chemical engineering, and more.
Pricing
| How Parallelstore pricing works | Pricing will be released when product is generally available. |
|---|---|
| Product | Pricing |
| Parallelstore | Contact sales for pricing |
How Parallelstore pricing works
Pricing will be released when product is generally available.
Parallelstore
Contact sales for pricing
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Understand the technical benefits of DAOS
Learn about parallel file systems for HPC
When to use parallel file systems
