imc summit 2016 breakout - gordon patrick - developments in persistent memory
TRANSCRIPT
DEVELOPMENTS IN PERSISTENT MEMORY GORDON PATRICK, DIRECTOR OF ENTERPRISE COMPUTING MEMORY, MICRON TECHNOLOGY
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See all the presentations from the In-Memory Computing Summit at http://imcsummit.org
Overview Why Persistent Memory (PM) What problems does PM solve?
Developing new markets Key Segment Focus Initial Case Studies
Designing in persistence Portfolio Considerations Ecosystem Requirements
DEVELOPMENTS IN PERSISTENT MEMORY
WHY PERSISTENT MEMORY?
3
MEMORY / STORAGE TECHNOLOGY HIERARCHY
Nanoseconds Microseconds Milliseconds
Storage I/O Memory Persistent Memory
Memory Control(Load/Store)
I/O Control(Read/Write)
Hard Disk Drive(Spinning Media)
SATA/SAS SSD(NAND)
NVME SSD(NAND)
NVME SSD(3D XPoint™
memory)
DIMMs(3D XPoint™
memory)ASP/Bit
Latency
NVDIMM-N(DRAM/NAND)
RDIMM/LRDIMM(DRAM)
Cost Optimized PM
Performance OptimizedPM
IMPACT OF PERSISTENT MEMORY ON APPLICATION PERFORMANCE
40ns
40ns
85,000ns
85,000ns
40ns
DRAM + NAND-Based NVMe SSD
DRAM + NVDIMM Block Mode
DRAM + NVDIMM Direct Mode
40ns
40ns
25,000ns
25,000ns
40ns
40ns
40ns
40ns
40ns
40ns NV Direct
NV Block
NVMe
LATE
NCY
Data committed to persistent media written to NAND through the I/O stack
Data committed to persistent media written to DRAM on NVDIMM-N through the I/O stack
Data committed to persistent media written to DRAM on NVDIMM-N through Load/Store Bus
DEVELOPING NEW MARKETS
DEVELOPING NEW MARKETS
Fast Persistent Writes Metadata StorageWrite Back Cache
Scale-out Storage VMware® VSAN Microsoft®
Azure™
Big Data Analytics HortonWorks® Cassandra™
In-Memory Databases SAP® HANA Microsoft® SQL Hekaton
Persistent Memory
Relational Databases
Microsoft® SQL MySQL™
Trademarked software named to identify primary segment applications. Their use does not represent an endorsement of Micron or Micron NVDIMM products.
ROAD TO PERSISTENCE: PHASE 1
Phase 1 primarily considers NVDIMM-N based solutions
Key focus includes performance optimized acceleration
DRAM-like latency Direct system access to DRAM but not flash Block or direct map driver Energy source for backup JEDEC defined
2016 2017 2018 2019 2020
Phase 1 Phase 2
Units
PERSISTENT MEMORY STACK Linux Driver provides
scalable application development
Applications dynamically partitioned across memory space
Key focus on direct access persistence
NVDIMM
Application
Driver
obfs
Block Mode
fs pml
Direct Access Mode
L/SL/SR/W
CPU
block
file
mem
pmem
R/W
Kernel
Byte AddressabilityBlock Addressability
File System PMEM LibrariesFile SystemObject
Early block-mode results 2X+ faster database logging performance
for Microsoft® SQL Server
Up to 4X+ faster SQL cluster replications when moving the log from NAND flash to HPE NVDIMMs4
2X+ faster transaction rates in Linux® applications when using HPE NVDIMMs
Up to 63% faster exchange speeds
CASE STUDIES: REAL-WORLD RESULTS
Source: HPE public data sheets and media interviews. HPE lab testing on a DL380 Gen9 Server with E5 2600 v4 processor and 8 GB HPE NVDIMM.
Source: Plexistor public case study. Dual socket XEON E5-2650v3, enterprise SATA SSD, 64GB DDR4 DIMM vs. 64GB DDR4 NVDIMM-N
MongoDB 6-9X latency improvement
Avg. La
tency
95th
perce
ntile
99th
perce
ntile
0100020003000400050006000
Linux XFS on Flash Plexistor on NVDIMM
Late
ncy
(µs)
DESIGNING IN PERSISTENCE
ROAD TO PERSISTENCE: PHASE 2
Latency
Endurance
Volatility
Cost
Low HighFeature Set Tradeoffs
DRAM Baseline
NAND Baseline
Cost-Driven Feature Set
Performance Feature Set
2016 2017 2018 2019 2020
Phase 1 Phase 2
Units
REQUIREMENTS & NEXT STEPS
Summary Performance optimized Persistent
Memory solutions available today Now established as a new
complementary category in the memory / storage hierarchy
Next phase of development based on emerging memory, controller design and protocols will drive full system persistence
Developers can maximize performance by taking advantage of direct access to persistent memory
NVDIMM
Application
Driver
obfs
Block Mode
fs pml
Direct Access Mode
L/SL/SR/W
CPU
R/WByte AddressabilityBlock Addressability