OPERA. Low Power Heterogeneous Architecture for the Next Generation of Smart Infrastructure and Platforms in Industrial and Societal Applications

Transcription

1 OPERA Low Power Heterogeneous Architecture for the Next Generation of Smart Infrastructure and Platforms in Industrial and Societal Applications Co-funded by the Horizon 2020 Framework Programme of the European Union Grant Agreement Number DSD, Cyprus, August 31 September 2, 2016 Alberto Scionti, Ph.D Istituto Superiore Mario Boella (ISMB)

2 Outline Motivations Consortium Project objectives OPERA approach to the computing continuum: Software layer Heterogeneity Ultra-low power embedded systems OPERA use cases OPERA Id DSD-2016, Cyprus, Aug 31 - Sep. 2

3 Motivations Growing demand for storage and processing capabilities in-the-cloud User generated contents. New applications and services. Advances in technology allowing the implementation of smart embedded systems (IoT). Convergence between traditional HPC and Cloud computing applications: Hybrid workloads (HPC+Cloud) are becoming the norm rather than the exception: Cloud computing technologies offer a simpler way to scale applications. HiPEAC vision for Advanced Computing in Horizon 2020 Many applications exploiting AI techniques (machine learning, neural networks, etc.).

4 Motivations Emerging of Cyber-Physical Systems (CPS): Integration of high computing capabilities w/ sensors and actuators: Real-time interaction with the surrounding environment. Producing an enormous amount of data to analyse. Integration of CPS with Cloud infrastructures to enable new services Energy efficiency and dependability for the computing continuum: Need more energy efficiency (increasing performance in traditional system means increasing energy consumption). HiPEAC vision for Advanced Computing in Horizon 2020 Need more dependability against defects, failures and malicious attacks.

5 Consortium The OPERA project leverages on the expertise of both industrial and academic partners to counter challenges on building next generation computer infrastructures. OPERA Id DSD-2016, Cyprus, Aug 31 - Sep. 2

6 Project Objectives - Next-Gen. Servers OBJ1) New generation servers: Integrating cutting-edge technologies to maximise performance on low power high-density next-gen. servers. OBJ2) Data centre-in-a-box: Build a prototype of a server chassis capable of hosting up to 16 server modules along with acceleration boards (e.g., FPGA-based boards) in a 1U form factor. OBJ3) Heterogeneous architectures: Combining ARM, X86_64, and OpenPower processing architectures along with FPGAs for task acceleration and energy efficiency. OBJ4) Workload decomposition: Splitting applications into independent tasks which are run on the most appropriate processing element to speed up their execution.

7 Project Objectives - Energy Efficiency OBJ5) Innovative power management: Find ways to manage power consumption in more holistic way by considering the system at whole to optimise the quest for energy efficiency. OBJ6) Metrics for energy efficiency: Determining the most appropriate metrics for evaluating/monitoring the energy efficiency.

8 Project Objectives - Embedded Systems OBJ7) Low power and ultra-low power architectures: Leveraging on specialised functions to increase system energy efficiency. OBJ8) Manycore on ULP devices: Exploiting the execution on ultra-low power (ULP) manycore processor to speedup computer vision algorithms. OBJ9) ULP devices for traffic monitoring: Build a prototype of a ultra-low power CPS able to work in critical conditions.

9 OPERA approach to the computing continuum COMPUTING CONTINUUM Embedded Devices & CPS Cloudlet Private Cloud Public Cloud Growing size, storage and computing capabilities Cloud servers HPE Moonshot (lightweight chassis): up to 16 mini servers in a 1U form factor, up to 4 acceleration boards (FHHL), two 10-GbE connections, internal SATA connections for storage. Integration of Power8-based servers for highly computational demanding tasks (CAPI + optical interconnections). Design of a high-performance FPGA-based acceleration board.

10 OPERA approach to the computing continuum COMPUTING CONTINUUM Embedded Devices & CPS Cloudlet Private Cloud Public Cloud Growing size, storage and computing capabilities Cloudlet/Gateway HPE Moonshot (small): up to 4 mini servers in a desktop-like form factor, up to acceleration boards (FHHL), SATA bays, large set of interconnections: Wired (1-GbE, USB, SIM cards). Wireless.

11 OPERA approach to the computing continuum COMPUTING CONTINUUM Embedded Devices & CPS Cloudlet Private Cloud Public Cloud Growing size, storage and computing capabilities Cyber-Physical System Ultra-low power single board computer system based on the ULP System-on-Chip provided by STMicroelectronics. Reconfigurable antenna.

12 OPERA Software Layer At the basis of the Workload Decomposition approach there is the usage of micro-services (uservices): Independent software modules. Exporting a common interface (e.g., HTTP-REST API) for communicating w/ other uservices. Composing a graph (workload) which describes the whole application. Described with TOSCA standard (XML-based). TOPOLOGY uservice 1 uservice 2 uservice 3 uservice 4 properties properties M1 capabilities requirements Node Type Relationship Type M2 cnd interfaces interfaces M3 Run on Linux systems as containers for a scalability. M4 PLAN

13 OPERA Software Layer OPERA Cloud stack relies on an extended version of OpenStack supporting workload decomposition: Use of a knowledge base to initially assign uservices to the most appropriate processing element. Traditional VMs API API API API Linux Containers Monitoring activity of various processing elements to redistribute the workload. Applying energy efficiency policies to steer the assignment. OpenStack Cloud Infrastructure LIBs LIBs LIBs LIBs uservice manager Knowledge Base

14 Leveraging on Heterogeneity Hardware heterogeneity as a way to get energy efficiency and computing power, by exploiting hardware specialisation: Same ISA but different micro-architectural implementations. Different ISA w/ different micro-architectural implementations. Dedicated circuits (ASICs and FPGAs). Hybrid solutions (e.g., CPU+GPU, CPU+FPGA) Hybrid solutions allow to obtain the maximum performance w/o reducing the platform flexibility (programmability opportunities).

15 Leveraging on Heterogeneity Why are FPGA so interesting for the Cloud computing domain? Integration of high-performance ARM cores with programmable logic. J. Larus, Keynote, HiPEAC Conf., Jan Many applications can benefit from dedicated circuits (e.g., implementing neural networks, etc.). Support for IEEE-754 floating point operations. Accelerator interface exposed through a Board Support Package (BSP). Software integration through OpenCL functions/kernels. An example of accelerating BING search queries.

16 Leveraging on Heterogeneity How to free FPGA computing power Cloud Environment TOSCA MODEL M1 API uservices (Linux Containers) M2 FPGA uservice uservice Manager OpenStack Cloud Infrastructure M3 Execution Plan ARM ARM Programmable Logic ARM Cortex-A9 (ARM_v7). Neon SIMD extension. Up to 1.2GHz. Out-of-Order execution pipeline. 32KiB + 32KiB I$ and D$. 512KiB L2 Cache. 256KiB Scratchpad on chip.

17 Leveraging on Heterogeneity How to move highly computational demanding tasks on Power8 API uservice API uservice ARM ARM OpenPOWER High-speed Low-latency Interconnection Cmd/Resp. (Coherency) Program. Logic (AFU) Network Functions Cmd/Resp. (Coherency) LL$ I/D$ I/D$ I/D$ PLS CAPP C0 C1 Cn CAPI interface (PLS+CAPP) can be used to issue portion of the application code to the highperformance OpenPOWER system, by exploiting interaction between ARM cores and programmable logic (PLS) on the FPGA board. External systems interact with the OpenPOWER by exploiting network functions accelerated on the programmable logic (AFU).

18 Ultra-Low Power Embedded System CPS (i.e., edge nodes of the Cloud infrastructure) require energy efficiency. To not limit performance, we envisioned an embedded platform as follows: Manycore for exploiting application parallelism. Embedded Manycore System ISA, microarchitecture and manufacturing process optimised for energy efficiency (few ua per FD-SOI). Optical Sensor Image Signal Processor EE core Large embedded RAM EE core. EE core Antenna Specialised functions for image processing acceleration (e.g., video compression). Enhanced IPU EE core EE core EE core EE core.. EE core EE core Low power reconfigurable antenna for radio communications Hardware functions supporting Convolutional Neural Networks (CNN). Convolutional Neural Net EE EE ISA,. microarchitecture and core core manufacturing technology optimised for energy efficiency EE core Fast support to image recognition and classification.

19 Ultra-Low Power Embedded System Smart wireless communication support through a reconfigurable radiofrequency antenna capable of: Steering the electromagnetic beam. Changing the used TX/RX frequency/polarisation (communication protocol adaptation). Incorporating advanced and flexible SDR functionalities. Low power working condition thanks to accurate design and integration of advanced components.

20 OPERA Use Cases Virtual Desktop Infrastructure Leveraging the Cloud concept of as-a-service to provide a virtualised desktop platform that can be accessed remotely. To this end OPERA will extensively use the OpenStack platform along with low power server solutions and FPGA accelerators. Road traffic monitoring Using ULP devices to monitoring traffic in urban context, with emphasis on detection of pedestrians, accidents, animals and cycles. High-density servers on truck Supporting the operation of Italian agency Protezione Civile, by delivering services for video/ i m a g e a c q u i s i t i o n a n d p ro c e s s i n g a n d transmission through satellite connections. OPERA Id DSD-2016, Cyprus, Aug 31 - Sep. 2

21 Questions