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Towards Exascale Computing

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Towards Exascale Computing TowardsTowards ExascaleExascale Computing:Computing: TheThe ECOSCALEECOSCALE ApproachApproach Dirk Koch, The University of Manchester, UK ([email protected]) 1 Motivation: let’s build a 1,000,000,000,000,000,000 FLOPS Computer (Exascale computing: 1018 FLOPS = one quintillion or a billion billion floating-point calculations per sec.) 2 1,000,000,000,000,000,000 FLOPS . 10,000,000,000,000,000,00 FLOPS 1975: MOS 6502 (Commodore 64, BBC Micro) 3 Sunway TaihuLight Supercomputer . 2016 (fully operational) . 12,543,6000,000,000,000,00 FLOPS (125.436 petaFLOPS) . Architecture Sunway SW26010 260C (Digital Alpha clone) 1.45GHz 10,649,600 cores . Power “The cooling system for TaihuLight uses a closed- coupled chilled water outfit suited for 28 MW with a custom liquid cooling unit”* *https://www.nextplatform.com/2016/06/20/look-inside-chinas-chart-topping-new-supercomputer/ . Cost US$ ~$270 million 4 TOP500 Performance Development We need more than all the performance of all TOP500 machines together! 5 TaihuLight for Exascale Computing? We need 8x the worlds fastest supercomputer: . Architecture Sunway SW26010 260C (Digital Alpha clone) @1.45GHz: > 85M cores . Power 224 MW (including cooling) costs ~ US$ 40K/hour, US$ 340M/year from coal: 2,302,195 tons of CO2 per year . Cost US$ 2.16 billion We have to get at least 10x better in energy efficiency 2-3x better in cost Also: scalable programming models 6 Alternative: Green500 Shoubu supercomputer (#1 Green500 in 2015): . Cores: 1,181,952 . Theoretical Peak: 1,535.83 TFLOPS/s . Memory: 82 TB . Processor: Xeon E5-2618Lv3 8C 2.3GHz . PEZY-SC accelerators (GPU-like that use OpenCL) (theoretical 6-7 GFLOPS/W) . 150 MW for Exascale (very optimistic) . Good, but not good enough 7 GPUs? . Energy efficiency is the key for performance computing high integration high memory / I/O throughput There is strong need to process more data faster at less power! . Energy efficiency is the most important technology driver (from mobile to datacenters) . Many people consider GPUs for energy efficient computing, but M2050 GPU TPD: 225 W (5.4 kWh/day) Equipment cost: 2400 US$ (energy cost exceeds in less than 5 years considering 0.25 US$/kWh) 1 TFLOPs peak 225 MW at Exascale (very optimistic) 8 Alternative: FPGAs Baidu's analysis on predictive search algorithms: . FPGA ~500 GFLOPS @ 20W . 40 MW at Exascale (optimistic) close to the green zone 9 CPUs vs. GPUs vs. FPGAs Scalar processing SIMD processing (e.g. GPU) m ul ST STST tith STST rea ACC x0 y0 ACC STx3 x2 x1 x0 y3 y2 y1 y0 d ACC STx3 x2 x1 x0 y3 y2 y1 y0 in ACC x3 x2 x1 x0 y3 y2 y1 y0 g ACC x3 x2 x1 x0 y3 y2 y1 y0 ROR k0 l0 ROR ACCk3 k2 kx13kx02 x1l3x0l2 ly13 ly02 y1 y0 ROR ACCk3 k2 kx13kx02 x1l3x0l2 ly13 ly02 y1 y0 ROR k3 k2 k1 k0 l3 l2 l1 l0 ROR k3 k2 k1 k0 l3 l2 l1 l0 ADD a0 b0 ADD RORa3 a2 ak13ak02 kb1 3kb0 2 bl13bl02 l1 l0 ADD RORa3 a2 ak13ak02 k1b3k0b2 bl13 bl02 l1 l0 ADD a3 a2 a1 a0 b3 b2 b1 b0 ADD a3 a2 a1 a0 b3 b2 b1 b0 MUL MUL ADD a3 a2 a1 a0 b3 b2 b1 b0 MULMULADD a3 a2 a1 a0 b3 b2 b1 b0 LD LD MULMUL LDLDMUL ADDST ADDST LDLD ADDSTADDST LD c0 ADDST c3 c2 c1 c0 MULACC MULACC ADDST c3 c2 c1 c0 MULACC ADDST c3 c2 c1 c0 MULACC c3 c2 c1 c0 MULACC c3 c2 c1 c0 MULACCMULACC c3 c2 c1 c0 Pipelining often better Dataflow processing / pipelining (FPGAs) for global states 10 CPUs vs. GPUs vs. FPGAs . CPUs are ideal for control flow dominated tasks (e.g., an OS, compilation) . GPUs are ideal for double precision number crunching . FPGAs are ideal for number crunching problems that fit dataflow processing model . FPGA advantages . Customized processing . Optimized data movement . High integration (entire system including memory, mass storage, networking, acceleration and CPU) . High performance at low power 11 We are at the end of CMOS scaling . Compute demand still exploding (Health, Climate, AI, Big data) . No alternatives (optical, quantum computing) at the horizon! We have to make the best out of CMOS! . Issue is not the number of transistors but energy efficiency Energy efficiency translates into compute performance! . Objectives are hard to meet with CPUs or GPUs Custom Computing using FPGAs! . Needs rethinking . Programming models . Operating systems (runtime environments) . The “ecosystem” 12 HPC FPGA Research in Manchester FPGA for HPC computing . Manchester participates with three joined H2020 projects: . ECOSCALE http://www.ecoscale.eu/ (Scalable programming environment and hardware architecture tailored to current and future HPC applications) Manchester: FPGA virtualization and run-time management, automatic generation of reconfigurable module libraries from OpenCL kernels, support for all recent Xilinx FPGAs (including Ultrascale+) 13 HPC FPGA Research in Manchester FPGA for HPC computing . Machester participates in further joined H2020 projects: . ExaNeSt http://www.exanest.eu HPC interconnection networks, storage and cooling . ExaNode http://www.exanode.eu Investigates and develops a highly integrated, high-performance, hetero- geneous System-on-a-Chip (SoC) aimed towards exascale computing . Budget: over 12M, ~2M for Manchester 14 HPC FPGA Research in Manchester FPGA for HPC computing . Follow-up project EuroEXA “Co-designed Innovation and System for Resilient Exascale Computing in Europe: From Applications to Silicon” . Budget: 20M, over 6M for Manchester 15 HPC FPGA Research in Manchester . Follow-up project EuroEXA — partners: . Spain — Barcelona Supercomputing Center . United Kingdom — ARM-UK, Iceotope, Maxeler Technologies, The University Of Manchester, The Hartree Centre of STFC, ECMWF (European Centre for Medium-Range Weather Forecasts) . Greece — FORTH, Synelixis Solutions Ltd . Belgium — IMEC . Sweden — ZeroPoint Technologies . Netherlands — Neurasmus . Italy — INFN (Istituto Nazionale Di Fisica Nucleare), INAF (Istituto Nazionale Di Astrofisica) . Germany — Fraunhofer-Gesellschaft 16 HPC FPGA Research in Manchester Manchester contribution: from OpenCL to configurable modules . Design and implementation of the tool flow backend (usable by software domain experts) . Develop the middleware for the management of reconfigurable blocks at runtime . Provide partial reconfiguration 17 HPC FPGA Research in Manchester Manchester contribution: . Runtime management of reconfigurable resources (i.e. operating system services) 18 HPC FPGA Research in Manchester Manchester contribution: . Case study: increment3d . Compiled out of the box . Relocatable to 8 positions (on a latest Kintex UltraScale+ FPGA) 19 GoAhead FPGA PR Tools from Manchester FPGA design tools for implementing partial reconfiguration (most advanced tools currently available) Spin-off planned (highly needed in datacenters, e.g., Amazon F1) 20 FPGA Database Accelerator . Build library with SQL operators config. port . Compose graph at run-time FPGA PCIe core local memory > + join sort mean ODBC ODBC application proxy database Maxeler implementation: . 512 bit datapath in > > > join + sort out . 300 MHz (Virtex-6) . Tables are stored in static design: PCIe, memory, filesystem, management, reconfiguration 24 GB on board RAM (48 GB possible) 21 FPGA Database Accelerator . Reconfigurable region . Regularly routed . 16 x 32-bit (512-bit total) . @ 300 MHz . ~40% of a MAX3 workstation is reconfigurable 22 FPGA Database Accelerator . Build library with SQL operators . Compose steam processing machine at runtime . Integrated in: 23 Further Research . Big data analytics machine (80 SSDs connected to 4 FPGAs) . Worlds fastest open source PCIe infrastructure . Large problem sorting . Resource elastic hardware . Real FPGA virtualization (in the space domain) . FPGA Interlays (FPGAs inside CPU cores) . FORTE: Functional Oxide Reconfigurable Technologies (Manchester: explore memristors for future FPGAs) 24 Contributors . Grigore Nicolae Bogdan (CDT) Query optimization, resource management, FPGA virtualization . Athanasios Stratikopoulos (ARM) FPGA virtualization . Malte Vesper (DSTL) SSD stream processing infrastructure, applications . Raul Garcia (Conacyt) Reconfigurable instruction set extensions . Christian Beckhoff (hobbyist) GoAhead support (tool for building reconfigurable systems) . Dr. Edson Horta, Khoa Pham (H2020: ECOSCALE) HLS support for PR and runtime management 25.
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