Computational Storage offers near-data acceleration, and it is gaining popularity with recent commercialization and standardization efforts. In this talk, we present how Computational Storage can be used to scale the performance of a key-value storage engine and deep learning training workloads. We propose a new key-value storage engine, named RETINA, where Computational Storage, Samsung SmartSSD, accelerates its data processing and user-defined processing pipelines. RETINA adopts a cross-layered approach between the host CPU and Computational Storage to leverage the host’s flexibility, fine-grained control and Computational Storage’s near-data acceleration. We show how RETINA can improve the performance and efficiency of key-value storage and deep learning workloads.
RETINA: Exploring Computational Storage (SmartSSD) Usecase
- Changwoo MinVirginia Tech
With the ongoing work in the CS TWG, the chairs will present the latest updates from the membership of the working group.
- Scott ShadleySolidigm Technology, SNIA
Learn what is happening in NVMe to support Computational Storage devices.
Computational Storage is a new field that is addressing performance and scaling issues for compute with traditional server architectures.
NVMe and SNIA are both working on standards related to Computational Storage. The question that is continually asked is are these efforts are compatible or at odds with each other.
This presentation looks at a computational storage use-case within the Human Cell Atlas genomics research and discovers that the deployed HW CS engine is insufficient and why this is the case.
The exploration of computation near flash storage has been prompted by the advent of network-attached flash-based storage enclosures operating at tens of gigabytes/sec, server memory bandwidths str
- Sean GibbEideticom
- Andrew MaierEideticom
Data center systems power consumption is currently one of the biggest concern and green computing is main industry interest.
- Yangwook KangSamsung Semiconductor, Inc.
Apache Ozone is a highly scalable distributed object storage system and also provides the file system interface.
Large-scale data analytics, machine learning, and big data applications often require the storage of a massive amount of data.
Computational storage in general can bring unique benefits in increasing the efficiency of CPU utilization in a data processing system.
We examine the benefits of using computational storage devices like Xilinx SmartSSD to offload the compression to achieve an ideal compression scheme where higher compression ratios are achieved wi
SNIA Developer Conference
September 16-18, 2024
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