welcome to the sssi pm & nvmdimm sig open call we will ......welcome to the sssi pm &...

Welcome to the SSSI PM & NVMDIMM SIG

Open CallWe will begin shortly

Computational StoragePart of the Evolving Ecosystem

10 May 19

Scott Shadley, Co-ChairNick Adams, Co-Chair

© 2019 Storage Networking Industry Association. All Rights Reserved.

Agenda

Introduction to Computational StorageHow long has this idea been around, Why Now?How the TWG was formedWhat is the link to PM/NVDIMM?

Computational Storage Working Group FocusTaxonomy is Key, need the right TLAsScope is Critical, Roadmap to successA look at architectures – including PM/NVDIMM Synergies

Solutions Today and PlannedA look at some current solutionsA view of people’s thoughts of future solutions

3

Why Do We Need Computational Storage?The Back Story and Introduction


Many Factors driving a Need for Computational Storage

5

Three motivating factors for using Edge Computing1. Preserve privacy

2. Reduce latency3. Be robust to connectivity issues

Near-Data Computation: Looking Beyond BandwidthPublished in: IEEE Micro ( Volume: 34, Issue: 4, July-Aug. 2014 )

https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=40

https://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=6871686


What is Computational Storage

When to USE Computational StorageLarge Data Transfers, PCIe is bottleneckData pre/post processing & analysis Data can bypass the host - video deliveryAbility to move Software App to Storage

When to USE NVDIMMCompute heavy with small data-transferSmall data compute - in-memory compute Little to no parallelism

6


The Evolution of Computational Storage

A delicate process to build an Ecosystem

Great ideas! Time was needed to build it Many technology papers exist around:

“Active Disks”, “CAFS”, “Near-Data”“In-Storage”, “In-Situ”, “Near-Storage”

So did some initial products!

7


How to Drive the Real Market

So what do we do now? The answer came through conversations at many companies…

“Hey aren’t we all working to the same goal?” Let’s work together to go forward!

And BoF… @ 7AM… DURING FMS was proposed8


Time for Impactful Growth

A packed room got together & agreed: Move forward, fast!Equal parts start-up, and mature HWThis group asked questions like? “Could we create a

design to solve problems by bringing the compute into storage?

“Could this greatly reduce I/O bus traffic & allow distributed processing?”

9


SNIA Gets Formally Involved

Multiple Computational Storage sessions

And another BoF, this time at 7PM, even more attended!

Launched at the SNIA SDC18 Event as the proper proving ground for the development of standardization work

10


Playing Nice Together is Needed!

Is this a solution replacing a solution?

Complimentary work to the pyramid

Another facet of advancement of compute

In-Memory is needed, but some work canbe offloaded all the way to storage!

11

Com

puta

tiona

l St

orag

e

So Now What?The Progression of the TWG


Compute, Meet Data

Based on the premise that storage capacity is growing, but storage architecture has remained mostly unchanged dating back to pre-tape and floppy…

How would you define changes to take advantage Compute at Data?

13


Let’s Go Fishing for Data

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Now What, How do we Begin?

Born Oct 2018, the Computational Storage Technical Working group had 10 Founding companies.

Interest and attention grew… Quickly…

Meetings moved to weekly (AM/PM)

Now the single largest SNIA TWG!!15


40+ Participating Companies128+ Individual Members

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Finding a Focus and Direction

Focus on a definition list to ensure we cover questions from users on what it is and what products there are

Drive to a Scope and path to universal usage modelToday we have custom… Tomorrow Standard… Sound Familiar

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A New Taxonomy is Needed

Computational Storage:Architectures that provide Computational Storage Services coupled to storage offloading host processing or reducing data movement.

Computational Storage Service (CSS)Fixed (FPCSS)General Purpose (GPCSS)

18


A New Product Category

Computational Storage Device (CSx)

Computational Storage Drive (CSD)Computational Storage Processor (CSP)Computational Storage Array (CSA)

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Co m pu t a t ional S t o r age Arr a y(Acc e ss v ia C S P and / o r di r e c t t o S t o r age )

O p t ional Pr o x iedS t o r age Acc e s s

O p t ional Di r e c tS t o r age Acc e s s

Co m pu t a t ional S t o r age D r i v e(Acc e ss v ia C S P and / o r di r e c t t o S t o r age )

Co m pu t a t ionalS t o r age Pr o c e ss o r

Co m pu t a t ionalS t o r age D r i v e

T r adi t iona lS t o r age De v i c e

Ho s t A gen t 1

F ab r i c (P C I e , E t he r ne t, e t c )

Ho s t A gen t 2 Ho s t A gen t N

S t o r ag e

I/ O I/ OM G N T M G N T I/ OM G N T

O p t ional Pr o x iedS t o r age Acc e s s

O p t ional Di r e c tS t o r age Acc e s s

I/ OM G N T I/ OM G N T

S t o r ag e

I/ OM G N T I/ OM G N T

Arra yCont r o l

C S

C S

C S

C S

C S

C S

C S

C S

Co m pu t a t iona lS t o r ag e

Pr o c e ss o r(s )


Pr o c e ss o r(s )


Pr o c e ss o r(s )


Pr o c e ss o r(s )

C SC SD r i v e r s C S

D r i v e r s C SD r i v e r

A nd /O r A nd /O r


Scoping What Can Be Done

Multiple F2F sessions have been focused on what we can accomplish and what we will leave for later

ManagementSecurity Operation

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Architectures Aligned

Define the high level Open architecture firstAddress interactions of compatible solutions

PM Media, PM SolutionsWork with NVDIMM platform adaptations

Then spin off the different interfaces, protocols, etc. to the right industry groups, such as scala.org, DMTF, and NVMe.

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NVMe for Computation: Software

CSDs, CSPs and CSAs Hardware

OS

libcsnvmeSPDK

ApplicationsManagement

nvme-clinvme-of

Userspace

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Solutions Today and TomorrowExamples from TWG Members


Many Paths to Computational Storage

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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-CSPUsed by the device-mapper to offload parity calculations for RAID.This can be combined with p2pdma to further offload IO

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CPU

PCIe Subsystem

DR

AM

. . .CMB

NVMe CSP NVMe SSDs

md-raid


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 hostsHigher Compute Utilization achieved by pooling CSPs for HostsHigher Storage Utilization achieved by pooling CSDs for CSPsCSDs and CSPs ability to do p2p data exchangeLarge, dynamically configurable, variety of Fixed Purpose CSDs will be available for each Host Large Scale Dynamically

Configurable CSA

FabreXTM

DRAMDRAMDRAMDRAM

. . .

CSPs CSDs

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AI Inference at the Storage

Generate Metadata database (e.g tags) over a large set of unstructured data locally with an integrated AI inference engineOperation may be:

Triggered by a host processorDone offline as a background task (batches)

Metadata database may be then used by upper layer Big Data Analytics software for further processingCan work both on direct attached storage or on remote over the network storageExamples: Video search, Ad insertion, Voice call analysis, Images, Text scan, etc

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Computational Storage Array

Network

CPUCSDs

.

.

.

.


Video Transcoder Processor

Computational Storage Processor (CSP) designed for scalable high-efficiency Video Encoding/TranscodingIntegrated with FFmpegLinear scalable encoding capability, offload CPU from heavy encoding computingSave 90% of power consumptionsDeterministic low latency

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PCIe Interface

Codensity™ Video TranscodersNVMe FP-CSP inside U.2 module, w/o SSD functions

Qty=1..N


Real-Time AI Genomics Improvement

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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

The Basic Local Alignment Search Tool (BLAST).

Compute in Storageremoves CPUs lack of bandwidth to the data


Hadoop: Job Throughput

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3.1 w/ EC (6+3)

All benchmarks configurations use HDD as main storage24 Mapper/Reducers per Datanode *9 = 216 total

Better performance on CSS reported with lower Mapper/Reducers possible

Datanode Config:Dual E5-2640v3, 128GB DRAM, 12*6TB

SAS HDD

…

14% ↓ vs. baseline116% Job Throughput


Compute Offload AND Flash Temp


Baseline: Compute & Storage I/O

Bound Compute Offload Only

Flash Temp Only

One per server, 9

total


In Summary – Call to Action

Computational Storage is a Real MarketCustomers are deploying today

Solutions exist and will continue to growMaking the ‘uniform’ helps adoption

Standardizing the host interaction is vitalWe NEED more Support from Users/SW Solutions

Working with all TWG/SIGs & initiatives is keyJoining forces and cross-membership adds to success

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Thank You!!www.SNIA.org/Computational

www.SNIA.org/forums/sssi/NVDIMM

http://www.snia.org/Computational

https://www.snia.org/forums/sssi/NVDIMM

welcome to the sssi pm & nvmdimm sig open call we will ......welcome to the sssi pm &...

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