SlideShare a Scribd company logo
Company Overview
June 2020
Introducing MemVerge
Shuki Bruck
XtremIO co-founder and
Caltech professor
Founded by:
Yue Li
Caltech post-doc and top researcher
on non-volatile memory
Charles Fan
VMware storage BU leader
and creator of VSAN
Introducing MemVerge
World-class team in Silicon Valley assembled from:
Digital Transformation is Widespread
91.1% of enterprises undergoing
DX in the next three years
More data-centric business models
will drive AI/ML-infused analytics
Performance and availability
implications for enterprise storage
Market evolution will drive demand
for persistent memory technologies
DX Maturity Distribution
IDC: Digital Transformation Driving New “Big Memory” Requirements
Realtime Workloads are on the Rise
Worldwide, data is growing
at a 26.0% CAGR, and in
2024 there will be 143
zettabytes of data created
By 2021, 60-70% of the
Global 2000 will have at
least one mission-critical
real-time workload
IDC: Digital Transformation Driving New “Big Memory” Requirements
PM Revenue Forecast, 2019 - 2023
$0.00
$500.00
$1,000.00
$1,500.00
$2,000.00
$2,500.00
$3,000.00
Revneue ($M)
2019 2020 2021 2022 2023
REVENUE($M)
$2609M
248% CAGR 2019-2023
$65M
IDC: Digital Transformation Driving New “Big Memory” Requirements
Byte-Addressable Only
Long Term Forecast
Emerging Memories Find Their Direction: Objective Analysis and Coughlin Associates
Emerging memories are well
on their way to reach $36
billion of combined revenues
by 2030.
3D XPoint memory’s sub-
DRAM prices are expected to
drive revenues to over $25
billion by 2030
PMEM
Big Memory Definition
• Enables the ability to run applications in
memory for improved performance and
efficiency
• Leverages byte addressable persistent memory media
• Includes enterprise-class data services to
handle tier 1 availability and management
requirements
• Runs as a software-based memory
virtualization layer on industry standard
hardware
• The technology enabler for mission-critical
real-time computing
IDC: Digital Transformation Driving New “Big Memory” Requirements
CAPACITY STORAGE
BIG MEMORY
(DRAM, PM)
COMPUTE
CAPACITY STORAGE
MEMORY
COMPUTE
PERFORMANCE
STORAGE
OLD MODEL BIG MEMORY
COMPUTING
Our BIG MEMORY vision
All applications live in memory
Our mission
Open the door to Big Memory
A world of abundance,
persistence and high availability
Why Not All Apps Can Run in Memory yet…
No Data Services
Crash recovery is slow
Not plug-and-play
App rewrite needed
Can’t share memory
Siloed in servers
12
MemVerge Memory Machine™
Software
Subscription
Low Latency
PMEM over RDMA
Memory Machine
Memory Machine
Virtualizes
DRAM & PMEM
Plug Compatible
No re-writes
Memory Data Services
Snapshot, Replication, Tiering
13
Big Memory = Optane + Memory Machine
Multiple Memory Machines form a
memory lake that makes memory abundant
RDMA let’s Memory Machines
communicate with ultra-low latency
Intel Optane makes
memory persistent
Memory Machine snapshots, replication, and lightning
fast recovery make persistent memory highly available
Single-Node Memory Machine™ Implementation
Transparent Memory Service
Persistent Memory Allocator
Distributed Persistent Memory Engine
Big Memory Programming Model
SDK
Network ReplicationInstant SnapshotMemory Tiering
Machine LearningTrading / Market Data
Pub/Sub
In-Memory Analytics HPC
In-Memory
Checkpointing
Multi-Node Memory Machine™ Implementation
Transparent Memory Service
Persistent Memory AllocatorCluster Manager RDMA Transport
Big Memory Programming Model
SDK
RDMA RDMA RDMA RDMA
Distributed Persistent Memory Engine
CloningInstant SnapshotMemory Tiering RDMA Replication
Machine LearningTrading / Market Data
Pub/Sub
In-Memory Analytics HPC
In-Memory
Checkpointing
Financial Services are Killer Apps
for Big Memory
Growing blast zone
Memory data services needed
Microseconds matter
In-Memory databases used
Data > memory
More capacity needed
17
Financial Services are Killer Apps
for Big Memory
Growing blast zone
Memory data services needed
Microseconds matter
In-Memory databases used
Data > memory
More capacity needed
Uses Cases: Real-Time Workloads
AI/ML analytics and
inferencing like fraud
detection and smart security
Latency-sensitive
transactional workloads such
as high-frequency trading
According to IDC, by 2021, 60-70% of the Global 2000 organizations will have at least one mission-critical real-
time workload. Below are just a few examples of use cases that are implementing Big Memory now.
Real-time big data analytics
in financial services,
healthcare, and retail
Objectives
1. Market data event stream published
to multiple subscribers with the
lowest latency
2. Achieve fairness between the
subscriber processes
3. Persist the event stream without
incurring significant performance
penalties
Real Time Market Data Pub/SubMemory Machine
Use Case #1
Publisher
Subscribers
Memory Lake
Solution
1. Memory Machine™ software writes market
data event stream to an in-memory bus
2. Background process commits the event
stream to Persistent Memory synchronously
or asynchronously
3. The event stream is replicated over RDMA
to memory of other servers
4. Subscriber processes across all servers
read the event stream with low latency
Real Time Market Data Pub/SubMemory Machine
Use Case #1
Results with 210 Subscribers
Real Time Market Data Pub/SubMemory Machine
Use Case #1
Results
Real Time Market Data Pub/SubMemory Machine
Use Case #1
0.5 uS
Avg. latency local host
3 uS
Avg. latency remote host
<2x avg.
99.9% tail latency
Problem
1. Need to run analytics, reporting or
dev/test but concerned about taking
performance hit on Primary
instance
2. Application takes a long time to
restart after crash or planned
shutdown
In-Memory Database
Cloning & Crash Recovery
Memory Machine
Use Case #2
Solution
1. Memory Machine takes instant snapshot,
as frequently as every 1 minute
2. In-Memory Cloning easily creates a read
replica of the primary instance using
snapshot plus log replay
3. Fast restart from the database crash
using snapshot plus log replay
In-Memory Database
Cloning & Crash Recovery
Memory Machine
Use Case #2
+
Snapshot
Clone
Replay
Restart
Results
In-Memory Database
Cloning & Crash Recovery
Memory Machine
Use Case #2
Zero
Performance hit
Every Minute
Fine grained Snapshots
Fast
Clone and Crash Recovery
Problem
1. When data is greater than the size
of DRAM, AI/ML performance
slows down dramatically
2. Memory-intensive inference jobs
take a long time to load and restart
Big Memory AI/ML InferenceMemory Machine
Use Case #3
Solution
1. Create big memory lakes consisting of
DRAM and PMEM to provide capacity
needed for all data including models and
embeddings
2. Fast data recovery and restart by using
in-memory data snapshot
Big Memory AI/ML Training and
Inference
Memory Machine
Use Case #3
Snapshot
Clone
Replay
Restart
Memory Lake
Test results: 1000 libs (1 million records) case
Big Memory AI/ML Training and
Inference
Memory Machine
Use Case #3
0
5
10
15
20
25
30
1 2 4 8 16 32 64 128 256
TPS
Mongo+DRAM Memory Engine
0
100000
200000
300000
400000
500000
600000
700000
800000
900000
1000000
1 2 4 8 16 32 64 128 256
Data access latency
Mongo+DRAM(us) Memory Engine(us)
Results
Big Memory AI/ML Training and
Inference
Memory Machine
Use Case #3
50%
Cost savings vs DRAM
Up to 4x
Transactions per second
Up to 100x
Lower latency
MemVerge Vision for Big Memory Industry
1. Persistent Memory will be mainstream and data Infrastructure will be
memory-centric.
2. Big Memory, consisting of PMEM and DRAM, will achieve petabyte-
scale over clusters of servers interconnected by next-gen memory
fabrics
3. Big Memory software will be needed to offer data services in memory,
and every application will be run in-memory.
By 2025…
MemVerge Vision for Big Memory Industry
Compute
Memory
Performance Storage
Capacity Storage
Compute
Big Memory
Capacity Storage
What happens in memory
stays in memory…

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MemVerge Company Overview

  • 2. Introducing MemVerge Shuki Bruck XtremIO co-founder and Caltech professor Founded by: Yue Li Caltech post-doc and top researcher on non-volatile memory Charles Fan VMware storage BU leader and creator of VSAN
  • 3. Introducing MemVerge World-class team in Silicon Valley assembled from:
  • 4. Digital Transformation is Widespread 91.1% of enterprises undergoing DX in the next three years More data-centric business models will drive AI/ML-infused analytics Performance and availability implications for enterprise storage Market evolution will drive demand for persistent memory technologies DX Maturity Distribution IDC: Digital Transformation Driving New “Big Memory” Requirements
  • 5. Realtime Workloads are on the Rise Worldwide, data is growing at a 26.0% CAGR, and in 2024 there will be 143 zettabytes of data created By 2021, 60-70% of the Global 2000 will have at least one mission-critical real-time workload IDC: Digital Transformation Driving New “Big Memory” Requirements
  • 6. PM Revenue Forecast, 2019 - 2023 $0.00 $500.00 $1,000.00 $1,500.00 $2,000.00 $2,500.00 $3,000.00 Revneue ($M) 2019 2020 2021 2022 2023 REVENUE($M) $2609M 248% CAGR 2019-2023 $65M IDC: Digital Transformation Driving New “Big Memory” Requirements Byte-Addressable Only
  • 7. Long Term Forecast Emerging Memories Find Their Direction: Objective Analysis and Coughlin Associates Emerging memories are well on their way to reach $36 billion of combined revenues by 2030. 3D XPoint memory’s sub- DRAM prices are expected to drive revenues to over $25 billion by 2030 PMEM
  • 8. Big Memory Definition • Enables the ability to run applications in memory for improved performance and efficiency • Leverages byte addressable persistent memory media • Includes enterprise-class data services to handle tier 1 availability and management requirements • Runs as a software-based memory virtualization layer on industry standard hardware • The technology enabler for mission-critical real-time computing IDC: Digital Transformation Driving New “Big Memory” Requirements CAPACITY STORAGE BIG MEMORY (DRAM, PM) COMPUTE CAPACITY STORAGE MEMORY COMPUTE PERFORMANCE STORAGE OLD MODEL BIG MEMORY COMPUTING
  • 9. Our BIG MEMORY vision All applications live in memory
  • 10. Our mission Open the door to Big Memory A world of abundance, persistence and high availability
  • 11.
  • 12. Why Not All Apps Can Run in Memory yet… No Data Services Crash recovery is slow Not plug-and-play App rewrite needed Can’t share memory Siloed in servers 12
  • 13. MemVerge Memory Machine™ Software Subscription Low Latency PMEM over RDMA Memory Machine Memory Machine Virtualizes DRAM & PMEM Plug Compatible No re-writes Memory Data Services Snapshot, Replication, Tiering 13
  • 14. Big Memory = Optane + Memory Machine Multiple Memory Machines form a memory lake that makes memory abundant RDMA let’s Memory Machines communicate with ultra-low latency Intel Optane makes memory persistent Memory Machine snapshots, replication, and lightning fast recovery make persistent memory highly available
  • 15. Single-Node Memory Machine™ Implementation Transparent Memory Service Persistent Memory Allocator Distributed Persistent Memory Engine Big Memory Programming Model SDK Network ReplicationInstant SnapshotMemory Tiering Machine LearningTrading / Market Data Pub/Sub In-Memory Analytics HPC In-Memory Checkpointing
  • 16. Multi-Node Memory Machine™ Implementation Transparent Memory Service Persistent Memory AllocatorCluster Manager RDMA Transport Big Memory Programming Model SDK RDMA RDMA RDMA RDMA Distributed Persistent Memory Engine CloningInstant SnapshotMemory Tiering RDMA Replication Machine LearningTrading / Market Data Pub/Sub In-Memory Analytics HPC In-Memory Checkpointing
  • 17. Financial Services are Killer Apps for Big Memory Growing blast zone Memory data services needed Microseconds matter In-Memory databases used Data > memory More capacity needed 17
  • 18. Financial Services are Killer Apps for Big Memory Growing blast zone Memory data services needed Microseconds matter In-Memory databases used Data > memory More capacity needed
  • 19. Uses Cases: Real-Time Workloads AI/ML analytics and inferencing like fraud detection and smart security Latency-sensitive transactional workloads such as high-frequency trading According to IDC, by 2021, 60-70% of the Global 2000 organizations will have at least one mission-critical real- time workload. Below are just a few examples of use cases that are implementing Big Memory now. Real-time big data analytics in financial services, healthcare, and retail
  • 20. Objectives 1. Market data event stream published to multiple subscribers with the lowest latency 2. Achieve fairness between the subscriber processes 3. Persist the event stream without incurring significant performance penalties Real Time Market Data Pub/SubMemory Machine Use Case #1 Publisher Subscribers Memory Lake
  • 21. Solution 1. Memory Machine™ software writes market data event stream to an in-memory bus 2. Background process commits the event stream to Persistent Memory synchronously or asynchronously 3. The event stream is replicated over RDMA to memory of other servers 4. Subscriber processes across all servers read the event stream with low latency Real Time Market Data Pub/SubMemory Machine Use Case #1
  • 22. Results with 210 Subscribers Real Time Market Data Pub/SubMemory Machine Use Case #1
  • 23. Results Real Time Market Data Pub/SubMemory Machine Use Case #1 0.5 uS Avg. latency local host 3 uS Avg. latency remote host <2x avg. 99.9% tail latency
  • 24. Problem 1. Need to run analytics, reporting or dev/test but concerned about taking performance hit on Primary instance 2. Application takes a long time to restart after crash or planned shutdown In-Memory Database Cloning & Crash Recovery Memory Machine Use Case #2
  • 25. Solution 1. Memory Machine takes instant snapshot, as frequently as every 1 minute 2. In-Memory Cloning easily creates a read replica of the primary instance using snapshot plus log replay 3. Fast restart from the database crash using snapshot plus log replay In-Memory Database Cloning & Crash Recovery Memory Machine Use Case #2 + Snapshot Clone Replay Restart
  • 26. Results In-Memory Database Cloning & Crash Recovery Memory Machine Use Case #2 Zero Performance hit Every Minute Fine grained Snapshots Fast Clone and Crash Recovery
  • 27. Problem 1. When data is greater than the size of DRAM, AI/ML performance slows down dramatically 2. Memory-intensive inference jobs take a long time to load and restart Big Memory AI/ML InferenceMemory Machine Use Case #3
  • 28. Solution 1. Create big memory lakes consisting of DRAM and PMEM to provide capacity needed for all data including models and embeddings 2. Fast data recovery and restart by using in-memory data snapshot Big Memory AI/ML Training and Inference Memory Machine Use Case #3 Snapshot Clone Replay Restart Memory Lake
  • 29. Test results: 1000 libs (1 million records) case Big Memory AI/ML Training and Inference Memory Machine Use Case #3 0 5 10 15 20 25 30 1 2 4 8 16 32 64 128 256 TPS Mongo+DRAM Memory Engine 0 100000 200000 300000 400000 500000 600000 700000 800000 900000 1000000 1 2 4 8 16 32 64 128 256 Data access latency Mongo+DRAM(us) Memory Engine(us)
  • 30. Results Big Memory AI/ML Training and Inference Memory Machine Use Case #3 50% Cost savings vs DRAM Up to 4x Transactions per second Up to 100x Lower latency
  • 31. MemVerge Vision for Big Memory Industry 1. Persistent Memory will be mainstream and data Infrastructure will be memory-centric. 2. Big Memory, consisting of PMEM and DRAM, will achieve petabyte- scale over clusters of servers interconnected by next-gen memory fabrics 3. Big Memory software will be needed to offer data services in memory, and every application will be run in-memory. By 2025…
  • 32. MemVerge Vision for Big Memory Industry Compute Memory Performance Storage Capacity Storage Compute Big Memory Capacity Storage
  • 33. What happens in memory stays in memory…