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Computing on Low Power SoC Architecture

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Presentation on theme: "Computing on Low Power SoC Architecture"— Presentation transcript:

1 Computing on Low Power SoC Architecture
Daniele Cesini – INFN-CNAF Andrea Ferraro – INFN-CNAF Lucia Morganti – INFN-CNAF GDB - 11/02/2015

2 Outline Modern Low Power Systems on Chip Computing on System on Chip
ARM CPU SoC GPU Low Power from Intel Conclusion Daniele Cesini – INFN-CNAF GDB - 11/02/2015

3 Low-Power System on Chip (SoCs)
Daniele Cesini – INFN-CNAF GDB - 11/02/2015

4 Where do I find a SoC? Mobile Embedded Daniele Cesini – INFN-CNAF
GDB - 11/02/2015

5 Vector vs Micro computing power
NEC SX-5 CRAY-1 HITACHI S820/60 NEC SX-ACE INTEL8086 MOS 6510 Pentium Pro INTEL i7 Why did microprocessors take over? They have never been more powerful… ….but they were cheaper, highly available and less power demanding Daniele Cesini – INFN-CNAF GDB - 11/02/2015

6 Vector vs Micro vs ARM based
Is history repeating? Daniele Cesini – INFN-CNAF GDB - 11/02/2015

7 ARM based processor shipment
ARM based processors are shipped in billions of units ARM licences the Intellectual Properties to manufactures …many manufactures …. Samsung (Korea), MediaTek (China), Allwinner (China), Qualcomm (USA), NVIDIA (USA), RockChip (China), Freescale (USA), Texas Instruments (USA), HiSilicon(China), Xilinx (USA), Broadcom(USA), Apple(USA), Altera(USA), ST(EU) , WanderMedia(Taiwan), Marvel(USA), AMD(USA)etc.. Daniele Cesini – INFN-CNAF GDB - 11/02/2015

8 Ok, but then....an iPhone cluster?
NO, we are not thinking to build an iPhone cluster We want to use these processors in a standard computing center configuration Rack mounted Linux powered Running scientific application mostly in a batch environment ..... Use development board... Daniele Cesini – INFN-CNAF GDB - 11/02/2015

9 ODROID-XU3 Powered by ARM® big.LITTLE™ technology, with a Heterogeneous Multi-Processing (HMP) solution 4 core ARM A cores ARM A7 Exynos 5422 by Samsung ~ 20 GFLOPS peak (32bit) single precision Mali- T628 MP6 GPU ~ 110 GFLOPS peak single precision 2 GB RAM 2xUSB3.0, 2xUSB2.0, 1x eth Ubuntu 14.4 HDMI 1.4 port 64 GB flash storage Power consumption max ~ 15 W Costs 150 euro! Daniele Cesini – INFN-CNAF GDB - 11/02/2015

10 Other nice boards ...and counting...
PandaBoard WandBoard DragonBoard Rock2Board SabreBoard CubieBoard Arndale OCTA Board Texas Instruments EVMK2H ...and counting... Daniele Cesini – INFN-CNAF GDB - 11/02/2015

11 Some specs TDP between 5W and 15W (EVMK2H > 15W) BOARD GFLOPS
SOC GFLOPS (CPU+GPU) Eth Model ARM IP GPU IP DSP IP FREESCALE (Embedded SoC) SABRE Board Freescale i.MX6Q ARM A9(4) Vivante GC2100 (19.2GFlops) 25 1Gb ARNDALE (Mobile SoC) Octa Board Samsung Exynos 5420 A15(4) A7(4) Mali-T628 MP6 (110Gflops) 115 10/100 HARDKERNEL Odroid-XU-E Exynos 5410 Imagination Technologies PowerVR SGX544MP3  (51.1 Gflops) 65 Odroid-XU3 Exynos 5422 A7(4) (HMP) ARM Mali-T628 MP6 (110 Gflops) 130 INTRINSIC DragonBoard Qualcomm Snapdragon 800 Qualcomm Krait(4) Adreno 330 (130Gflops) 145 TI (Embedded SoC) EVMK2H TI Keystone 66AK2H14 A15(2) MS320C66x (189Gflops) 210 1Gb (10Gb) TDP between 5W and 15W (EVMK2H > 15W) Daniele Cesini – INFN-CNAF GDB - 11/02/2015

12 NVIDIA JETSON K1 First ARM+CUDA programmable 4 cores ARM A15 CPU
GPU-accelerated Linux development board! 4 cores ARM A15 CPU 192 cores NVIDIA GPU  300 GFLOPS (peak sp) ...for less than 200 Euros Daniele Cesini – INFN-CNAF GDB - 11/02/2015

13 CPU GFLOPS/Watt Daniele Cesini – INFN-CNAF 10/12/2014

14 GPU acceleration in K1 2xE5-2640+1xK40 ~ 3 GFLOPS/W dp ~ 9 GFLOPS/W sp
4 core ARM A15 ~ 18 GFLOPS Kepler SMX1 192 core ~ 300 GFLOPS ~ 15 Watt ~ 21 GFLOPS /W N.B. Single precision – 32 bit architecture ~ 1.5 GFLOPS /€ (0.67 €/GFLOPS) 2xE xK40 ~ 3 GFLOPS/W dp ~ 9 GFLOPS/W sp Daniele Cesini – INFN-CNAF GDB - 11/02/2015

15 How do you program them? (in a Linux environment)
GCC+OpenMP+MPI available for ARM architectures OpenCL for the GPU If you are lucky enough to find working drivers CUDA available only on the Jetson K1 Computing capability 3.2 (vs 3.5) Cross compilation GCC5+OpenMP4 tests ongoing… Daniele Cesini – INFN-CNAF GDB - 11/02/2015

16 GPU acceleration in scientific computation
2 x (E5-2673v2 (IvyBridge) 8 cores) ~ 2 x100 = 200 GFLOPS (double precision) 2 x 110 Watt = 220 W ~ 1 GFLOPS/W 1xNVIDIA TESLA K40 2880 cores 12 GB RAM ~ 1400 GFLOPS (double precision) ~ 4300 GFLOPS (single precision) 235 Watt ~ 6 GFLOPS / W dp ~ 18 GFLOPS/W sp CPU+GPU ~ 3 GFLOPS/W dp ~ 9 GFLOPS/W sp Daniele Cesini – INFN-CNAF GDB - 11/02/2015

17 Limitations Commodity SoCs and development boards have a number of limitations: 32 bit Small caches Small RAM size in the boards (O(2GB)) However modern SoCs can address 40bit No ECC memory Frequent failures and system crashes Slow connections (10/100Mb eth) in many cases Ethernet via USB in same boards HW bugs Daniele Cesini – INFN-CNAF GDB - 11/02/2015

18 Gbit Ethernet While latency was comparable to a server class 1Gb ethernet card (50/75 us) Daniele Cesini – INFN-CNAF GDB - 11/02/2015

19 OpenMP π computation CPU ONLY x20 Daniele Cesini – INFN-CNAF
GDB - 11/02/2015

20 Prime numbers computation
CPU ONLY x 4.6 Daniele Cesini – INFN-CNAF GDB - 11/02/2015

21 CMS 2014 results CPU ONLY High Energy Physics MonteCarlo simulations
David Abdurachmanov et al 2014 J. Phys.: Conf. Ser doi: / /513/5/052008 ? ARM slower by a factor 3 or 4 but… …ARM better by a factor 3 or 5 on the power ratio Daniele Cesini – INFN-CNAF GDB - 11/02/2015

22 FFT on CPU and GPU fftw3 on CPUs cuFFT on GPUs
Daniele Cesini – INFN-CNAF GDB - 11/02/2015

23 Molecular Dynamics on Jetson-K1
Parallel application for CPU and GPU: real life use case with GROMACS CPU and GPU Lower is better 1 jetson MPI 1 jetson CUDA 2 jetson MPI Jetson-K1 about 10X slower using the same number of CPU cores Jetson-K1 about 10X slower using the GPU (vs. an NVIDIA Tesla K20) Jetson-K1 13.5Watt Xeon+K20 ~320Watt GDB - 11/02/2015 Daniele Cesini – INFN-CNAF

24 Filtered Backprojection
CPU and GPU On (2xE K20): images analyzed in 1 hour: Wh (GPU not fully loaded) On 5xJetson-K1: images analyzed in 1 hour: Wh Daniele Cesini – INFN-CNAF GDB - 11/02/2015

25 Lattice Boltzmann on the Tegra K1
GPU only (*) On Tegra-K1 (preliminary) 15 GFLOPS GB/s Pe~ 10 Watt 40x slower than a K20m Porting easier than expected Performance under investigation (*) Schifano et al. ; A portable OpenCL Lattice Boltzmann code for multi- And many-core processor architectures; Procedia Computer Science Volume 29, 2014, Pages 40-49, doi: /j.procs Daniele Cesini – INFN-CNAF GDB - 11/02/2015

26 Only ARM based SoCs? And Intel?
INTEL produce SoCs Probably you have one in your laptop Some of them are low power Already 64bit Integrated GPU CILK++ programmable OpenCL programmable Daniele Cesini – INFN-CNAF GDB - 11/02/2015

27 Some low power from Intel
Daniele Cesini – INFN-CNAF 10/12/2014

28 2 cores + GPU 4.5 Watt (TDP) Intel HD Graphics OpenCL 2.0 Support
(*) (**) Daniele Cesini – INFN-CNAF GDB - 11/02/2015

29 AVOTON on HP Moonshot - HS06
(data from ) Daniele Cesini – INFN-CNAF GDB - 11/02/2015

30 HS06 HS06 on Exynos5, TegraK1 and Atom C2750 – Per core loaded
(data from ) Daniele Cesini – INFN-CNAF GDB - 11/02/2015

31 Test on Intel AVOTON Eth interconnect in both machines
N.B. – Comparison with an old Penryn Xeon CPU Daniele Cesini – INFN-CNAF 10/12/2014

32 Conclusion Mobile and embedded low power System-on-Chip are becoming attractive for scientific computing In particular if you manage to extract power from the GPU For selected applications Image processing No high RAM/RAM bandwidth requirements They still have many limitations for a production environment 32bit, no ECC, bugs, system stability, etc.. (BUT we used development boards - not server grade machines) NVIDIA K1 in our tests was the most powerful ARM based SoC Easy to install and use Easy to port CUDA based applications Intel has interesting low power SoCs Avoton has a high HS06/W ratio Looking forward to development boards based on 64bit SoCs with an ARM CPU on board Daniele Cesini – INFN-CNAF GDB - 11/02/2015

33 Links and contacts http://www.cosa-project.it
/slides/0.pptx /slides/0.pdf Daniele Cesini – INFN-CNAF GDB - 11/02/2015

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