A Dwarf in a Giant Embedded systems in High Performance Computing. Andrea Bartolini

Transcription

1 A Dwarf in a Giant Embedded systems in High Performance Computing Andrea Bartolini a.bartolini@unibo.it

2 2 HPC High Performance Computing High-performance computing (HPC) is the use of parallel processing for running advanced application programs efficiently, reliably and quickly Typical users: scientific researchers, engineers, data analysts

3 Structural calculations Computational fluid dynamics Climate and weather research Geophysics Molecular dynamics Lattice QCD Astrophysics Data analytics Machine learning..and many more Typical applications

4 Clusters / HPC Clusters/HPC: Composed of a collection of Desktop Computers and/or Servers which are called nodes The computing nodes are interconnected by a local area networks All the nodes works together as they was a single computer. Each node has its own operating system and communicate with network protocols (eg: ethernet, infiniband) Warehouse scale computers (but also datacenter) are sets of clusters composed of up to ~10/ nodes

5 Cost and performance Total cost of ownership (TCO) CAPEX capital costs IT provisioning Cooling infrastructure provisioning Facility and Power provisioning OPEX operational costs IT and Cooling Energy Costs Worker Salary In supercomputing CAPEX is 2-4x OPEX => Performance & availability Matters!!!

6 Why do we care - today

7 Why do we care - tomorrow

8 Why do we care - tomorrow How we can increase Power and Cost efficiency?

9 New Trend: Datacentre Automation 1 Common Interface Scalable Moniitoring Framework Machine Learning Heterogeneous Sensors Job Scheduling 3 Performance Analysis Energy efficiency Resources Management Data Visualization PDU ENV. CRAC CLUSTER 2 Reactive and Proactive Feedbacks

10 1 The Big Data & DL backbone Computing clusters Not only the computing engine of Big Data solution Also a complex industrial plant and a growing industry in ER A compute nodes can produces ~ 100/1000 metrics/s * peta/exa scale = Big Data! Datacenter automation improve energy/cost efficiency and effectiveness Industry 4.0 thanks to: Live collection and processing of large telemetry data (>100GB/day x cluster) On-line generation of plant models a.k.a. digital tweens, security break detection and HW fault prediction! Node0 Worker RDD Node0 P. Model Executor Cassandra node 1 MQTT2KairosDB Broker 1 Worker Executor Apache Spark NoSQL DB MQTT Brokers CLUSTER Worker Executor Node0 Node1 NodeN Node1 Node1 P. Model NodeN P. Model Cassandra node M MQTT2kairosDB Broker M NodeN

11 D.A.V.I.D.E. SUPERCOMPUTER (Development of an Added Value Infrastructure Designed in Europe) D.A.V.I.D.E. (#18 Green500 Nov 17)

12 D.A.V.I.D.E. SUPERCOMPUTER (Development of an Added Value Infrastructure Designed in Europe) OCP form factor compute node based on IBM Minsky 4x P100 HSMX2 Tesla 2 x POWER8 with NVLink BusBar 2xIB EDR LIQUID COOLING DiG 2 ETH Zurich / University of Bologna OUT-OF-BAND HIGH RESOLUTION POWER AND PERFORMANCE MONITORING

13 DiG: High Resolution Out-of-band Power Monitoring Beaglebone Black Out-of-band Zero overhead Collect more than 1.5 ks/s, 7/7d, 24/24h, for all users Architecture independent (i.e. tested on Intel, ARM and IBM) Fine grain down to ms scale ks/s + avg) IoT communication technology (MQTT) scalable Edge Computing! 7 Departement Informationstechnologie und Elektrotechnik

14 DiG: live FFT on the power traces Real-time Frequency analysis on power supply and more a live oscilloscope Application 1 Application 2 14 User -> to discriminate application phases Sys Admin -> to detect malicious users Designers -> to debug and optimize power delivery network 14 Departement Informationstechnologie und Elektrotechnik

15 AI+Big Data on D.A.V.I.D.E.: Anomaly detection 3 Fault!!

16 Datacenter monitoring network Simon Benninger 16

17 Processors power and thermal control 2

18 Available Projects : 1. IoT/Embedded systems and Big-data based monitoring devices for large scale networks 2. Deep learning and AI on embedded systems for datacenter automation 3. Signal processing and analysis of large scale data collection 4. Power control policies for upcoming large scale syestems What you will learn: 1. Design and use the technologies for big data and AI, at the base of the Industry 4.0 revolution 2. Work on a real datacenter and large scale cluster 3. HW and SW components at the base of future datacenter machines What you need to know: - Good mixture of HW and SW skills Andrea Bartolini a.bartolini@unibo.it