Computational storage in general can bring unique benefits in increasing the efficiency of CPU utilization in a data processing system. In this presentation we discuss the benefits of leveraging computational storage for offloading compute intensive tasks of object storage applications in a disaggregated storage environment. We demonstrate the ability of the solution to complement the CPU by taking away tasks that benefit from in-situ processing within the storage, thereby improving the overall system performance while lowering the TCO. This model has additional benefits of reducing data movement, resulting in better network utilization and decreased latency for supporting data analytics. Disaggregated storage is particularly attractive when using computational storage since scaling storage naturally yields to scaling of tasks that can be accelerated. We discuss results from our experimentation accelerating the S3 Select functionality and other use cases, using our High Performance Object Storage (HPOS) platform.
Accelerating Near Real-time Analytics with High Performance Object Storage
- Understanding the performance improvements that can be realized by using computational storage for accelerating compute intensive tasks.
- Understanding the advantages of using disaggregated storage model with computational storage devices in improving the scalability of solutions.
- Understanding the TCO and power benefits from using computational storage devices.
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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.
Computational Storage offers near-data acceleration, and it is gaining popularity with recent commercialization and standardization efforts.
- Changwoo MinVirginia Tech
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.
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 15-17, 2025
SDC 2025 is brought to you by SNIA. SNIA is an industry association committed to its mission of worldwide leadership developing and promoting architectures, standards, education and vendor-neutral collaboration.
