hartmut schultze may 15th, 2019 - hewlett packard …...graph inference 100x faster memory-driven...
Post on 27-May-2020
3 Views
Preview:
TRANSCRIPT
The DZNE
One center, several locations
75
433
755
9331031
1120
2009 2011 2013 2015 2017 2018
Number of staff
81 research groups
Scientists from
54 countries
34 % Internationalscientists
4019 Publications(till Dec. 2017)
Development cycle in systems medicine
+ Knowledge+ Understanding
+ Therapy+ Risk minimization
Patients
High Throughput
Big DataAnalytics
Models,diagnostic
tools
Validation
Clinical Data Laboratory
Imaging
SoC
SoC
SoC
SoC
Memory
Mem
ory
Memory
Mem
ory Memory-Driven
ComputingProcessor-Centric
Computing
Persistent Memory
+Fabric
Classical vs. Memory-Driven Computing architecture
Today’s architecture
is constrained by the CPU
DDR
Ethernet
PCI
If you exceed what can be connected to one CPU, you need another CPU
Memory-Driven Computing:
Mix and match at the speed of memory
SATA
Photonic fabrics destroy distance
Photonics
Hundreds of racks can behave as a single server
= 4096 yottabytes (=4096*1024 Bytes)292
bytes
use case - genomic data analysis
Can we make the processing of the raw genome data faster with Memory-Driven Computing?
▪ Using Superdome X
▪ Memory-Driven Computing -Architecture
▪ Small Change in Code
Near optimal RNA-Seq quantification
13secprocessing time reduced from22 minutes
112xincrease in analytics speed removes research bottlenecks
60%energy reductioncuts research costs
Change Mindset
–Think of having ubiquitous memory
–Think of access to memory having no interference from other nodes
–Rethink code
–Change mindset on approach
Transform performance with Memory-Driven programming
14
In-memory analytics
15xfaster
New algorithms Completely rethinkModify existing
frameworks
Genomics
100xfaster
Financial models
10,000xfaster
Large-scale
graph inference
100xfaster
Memory-Driven ComputingSoftware Defined - dynamically configure right-sized solutions in real time
Processors
Microprocessor/
System on Chip (SoC)
Memorydata storage, volatile and non-
volatile
Storage Class Memory/
Persistent Memory
Non-volatile, fast
Flash
Non-volatile, slow
DRAM
Volatile, fast, high energy
consumption
AcceleratorsSpecialized processors designed to perform
some functions more efficiently
GPU
FPGA
Array of logic gates that can be hardware-
programmed to fulfill user-specific tasks
Dot Product Engine/
Neuromorphic Computing
high performance per-watt task-specific
analog accelerator computing using
Memristor Arrays
EDGE DEVICE
HYPERCONVERGED
SYSTEMExascale
Eliminate data movement via shared memory and reduce energy consumption
Fabric
CPUs Accelerators
Memory technologies I/O
– High bandwidth
– Low latency
– Advanced workloads & technologies
– Scalable from IoT to exascale
– Compatible
– Economical
– Supports electrical or optical interconnects
– Open standard
– Security built-in at the hardware level
Gen-Z: new open interconnect protocolKey enabler of the Memory-Driven Computing open architecture
FPGA
GPUSoC ASICNEURO
Memory
Memory
Network Storage
Direct Attach, Switched, or Fabric Topology
NVM NVM NVM
SoC
Memory
Open consortium with broad industry support
Consortium Members
System OEM
Cisco
Cray
Dell EMC
HPE
Huawei
Lenovo
NetApp
Nokia
Yadro
CPU/Accel
AMD
Arm
Cavium
IBM
Qualcomm
Xilinx
Mem/Storage
Everspin
Micron
Samsung
Seagate
SK Hynix
Smart Modular
Spintransfer
Toshiba
WD
Silicon
Broadcom
IDT
Mellanox
Microsemi
IP
Avery
Cadence
Intelliprop
Mobiveil
PLDA
Eco/Connect
Alpha Data
AMP
FIT
Hirose
Jess Link
Keysight
Lotes
Luxshare
Molex
Senko
TE
Tyco
Software
Redhat
Vmware
Govt/Univ
ETRI
Oak Ridge
Simula
UNH
Yonsei
Technology
Service Provider
Microsoft
HPE Superdome FlexThe undisputed leader in in-memory computing
18
Designed with Memory-Driven Computing principles
Turn data into actionable insights in real time
– Unparalleled scale 4-32 sockets, 768GB-48TB+ memory
– Highly expandable for growth; ultra-fast fabric
Keep pace with evolving business demands
– Unique modular 4-socket building block
– Open management and hard partitioning for hybrid IT consumption
Safeguard mission-critical workloads
– Proven Superdome RAS with 99.999% single system availability
– Mission critical expertise with HPE Pointnext services
Pushing the limits
– Software-Defined Scalable Memory project in development to extend to 96TB of
composable memory across multiple racks
Software-Defined Scalable Memory on HPE Superdome FlexDeploying in our Memory-Driven Computing Sandbox
–
–
–
–
top related