MaxLinear partners with Los Alamos lab on storage acceleration

MaxLinear Inc. (NASDAQ: MXL) announced a collaboration with Los Alamos National Laboratory to develop hardware-accelerated OpenZFS file system storage for high-performance computing environments.

The partnership combines Los Alamos' Direct I/O support and ZFS Interface for Accelerators framework with MaxLinear's Panther Storage Accelerator system-on-chip technology. The collaboration aims to improve performance and storage capacity for NVMe flash-based storage infrastructure.

Testing results showed performance improvements of approximately 39 times for write operations and 7 times for read operations compared to standard ZFS implementations. The system achieved 57 GB/s read speeds and 47 GB/s write speeds with GZIP compression, compared to baseline ZFS performance of 1.2 GB/s writes and 8.1 GB/s reads.

"Los Alamos' Direct I/O support and Z.I.A. work were developed to accelerate performance for the ZFS-using community," said Gary Grider, Senior Director for Computing Technologies at Los Alamos National Laboratory. "MaxLinear demonstrated hardware-offloaded ZFS operations with reported speedups that illustrate the potential for accelerator-based approaches to reduce host CPU involvement."

The hardware-accelerated system provides data compression, encryption, deduplication and data protection services executed inline in hardware. The technology is designed to reduce host CPU overhead while maintaining ZFS data integrity guarantees.

Los Alamos National Laboratory operates ZFS storage systems at scale and has developed filesystem extensions including Direct I/O support and the ZFS Interface for Accelerators framework. MaxLinear contributes its Panther family of Storage Accelerator system-on-chips and software development kits.

The collaboration integrates Panther as a Data Processing Unit Services Module provider within the ZFS architecture, enabling hardware acceleration of CPU-intensive operations such as data compression and checksum generation.

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