1. Open Source Activity Showcase Computational Storage SNIA SwordfishTM

2. SNIA-at-a-Glance

3. Standards Development

4. Areas of Focus
Our areas of focus are well-aligned with both existing and emerging technologies, and with the needs of legacy and of emerging IT infrastructures. Each of the nine category areas have technical work associated with them focused on standards and specifications (see slide 5). The SNIA website home page has links to each of these areas so that you can dive into the specifics of SNIA activities.
SNIA also has the critical mass of vendors, and the appropriate structure, IP policies, and experience to add new categories, new technologies and new projects as the need arises
© 2018 Storage Networking Industry Association. All Rights Reserved.

5. 2019 Technical Work Group Activity
For 2018, we have an extensive technical agenda and committed roadmap of new standards. is the launching place to view current work, groups supporting it, draft and published standards and specifications.
© 2018 Storage Networking Industry Association. All Rights Reserved.

7. Technical Working Group Member Use Cases
Storage Sessions Friday in Room 212AB
SNIA TWG Intro :20 – 8:40
Industry Panel :00 – 4:55

8. 32 Participating Companies 100 Individual Members

9. NVMe FP-CSP RAID Offload
NVMe based Computational Storage Processor (CSP) advertises Fixed Purpose accelerator capable of RAID parity generation.
Operating System detects the presence of the NVMe FP-CSP
Used by the device-mapper to offload parity calculations for RAID.
This can be combined with p2pdma to further offload IO
CPU
PCIe Subsystem
DRAM
. . .
CMB
NVMe CSP
NVMe SSDs
md-raid

10. Efficient Utilization of Compute
Systems requiring similar sets of Computational Units can take advantage of large scale dynamically configurable CSAs utilizing FaberXTM network.
Large scale CSA shared by multiple hosts
Higher Compute Utilization achieved by pooling CSPs for Hosts
Higher Storage Utilization achieved by pooling CSDs for CSPs
CSDs and CSPs ability to do p2p data exchange
Large, dynamically configurable, variety of Fixed Purpose CSDs will be available for each Host
Large Scale Dynamically Configurable CSA
FabreXTM
DRAM
. . .
CSPs
CSDs

11. AI Inference at the Storage
Generate Metadata database (e.g tags) over a large set of unstructured data locally with an integrated AI inference engine
Operation may be:
Triggered by a host processor
Done offline as a background task (batches)
Metadata database may be then used by upper layer Big Data Analytics software for further processing
Can work both on direct attached storage or on remote over the network storage
Examples: Video search, Ad insertion, Voice call analysis, Images, Text scan, etc
Computational Storage Array
Network
CPU
CSDs .

12. Video Transcoder Processor
Computational Storage Processor (CSP) designed for scalable high-efficiency Video Encoding/Transcoding
Integrated with FFmpeg
Linear scalable encoding capability, offload CPU from heavy encoding computing
Save 90% of power consumptions
Deterministic low latency
PCIe Interface
Codensity™ Video Transcoders NVMe FP-CSP inside U.2 module, w/o SSD functions
Qty=1..N

13. Real-Time AI Genomics Improvement
The Basic Local Alignment Search Tool (BLAST).
Compute in Storage removes CPUs lack of bandwidth to the data
DNA and Protein alignment Database Management
Up to 100% more performance at no cost in CPU or Memory
Resolves the IO Bottleneck between CPU and Storage

14. Hadoop: Job Throughput Improvement
14% ↓ vs. baseline
116% Job Throughput
23% ↓ vs. baseline
131% Job Throughput
Compute Offload
AND Flash Temp
37% ↓ vs. baseline
160% Job Throughput
Baseline:
Compute & Storage I/O Bound
Compute Offload Only
Flash Temp Only …
One per server, 9 total
3.1 w/ EC (6+3)
Datanode Config:
Dual E5-2640v3, 128GB DRAM, 12*6TB SAS HDD
All benchmarks configurations use HDD as main storage
24 Mapper/Reducers per Datanode *9 = 216 total
Better performance on CSS reported with lower Mapper/Reducers possible