cloud, distributed, embedded: erlang in the heterogeneous computing world
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Cloud, Distributed, Embedded. Erlang in the Heterogeneous Computing World
Omer Kilic || @OmerK
omer@erlang-solutions.com
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Outline
• Challenges in modern computing systems
• Heterogeneous computing
• Co-processors and accelerators
• Programming models and tools
• Alternate architectures
• Parallella Vision System
• Erlang Embedded Project
• Q&A
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Challenges: Software
• Frequency wall
• Memory bottlenecks
• Software complexity
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Amdahl’s Law
• “…the maximum speed-up through parallel processing is set by the amount of code which has to run serial”
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Challenges: Hardware
• Yield issues
• Wiring and interconnect
• Thermal density
•Power consumption
End of Moore’s law imminent…
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Challenges
“With nearly 10 billion devices connected to the internet and predictions for exponential growth, we’ve reached a point where the space, power, and cost demands of traditional technology are no longer sustainable.”
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Meg Whitman President and CEO, HP
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Internet of Things
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Device Architectures (I)
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Device Architectures (II)
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Heterogeneous Computing (I)
• Special purpose, highly specialised architectures will outperform general purpose processing devices
– Possibly by orders of magnitude
– In terms of energy efficiency as well as raw speed
– Parallel execution is key
• Non-programmable/pseudo-programmable accelerators: ASIC, DSP, GPU, …
• Fully programmable accelerators: FPGAs
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Open Compute Project
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Heterogeneous Computing (II)
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GPUs
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Anatomy of a GPU
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Co-processors: NetFPGA 10G
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Co-processors: Generic COTS devices
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Landscape of accelerator programming
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Interface CUDA OpenCL DirectCompute RenderScript
Originator NVIDIA Khronos (Apple) Microsoft Google
Year 2007 2008 2009 2011
Area HPC, desktop Desktop, mobile, embedded, HPC
Desktop Mobile
OS Windows, Linux, Mac OS
Windows, Linux, Mac OS (10.6+)
Windows (Vista+) Android (3.0+)
Devices GPUs (NVIDIA) CPUs, GPUs, custom
GPUs (NVIDIA, AMD)
CPUs, GPUs, DSPs
Work unit Kernel Kernel Compute shader Compute script
Language CUDA C/C++ OpenCL C HLSL Script C
Distributed Source, PTX Source Source, bytecode LLVM bitcode
From: “The landscape of accelerator programming: a view from ARM”, Lokhmotov, A., 3rd UK GPU Computing Conference, London
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Accelerator types
• Programmable accelerators
– CPU Vector extensions: x86/SSE/AVX, PowerPC/VMX, ARM/NEON
– GPUs supporting general-purpose computing (GPGPUs)
– Sony/Toshiba/IBM Cell (Sony PlayStation 3, HPC)
– ClearSpeed CSX (HPC, embedded)
– Adapteva Epiphany (HPC, mobile)
– Intel MIC (HPC)
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Programming accelerators
• Proprietary low-level APIs, typically C-based:
– Vector intrinsics
– NVIDIA CUDA
– ATI Brook+
– ClearSpeed Cn
• No software portability, obsolescence risk.
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OpenCL (I)
“OpenCL (Open Computing Language) is an open, royalty-free standard for general-purpose parallel programming of heterogeneous systems. OpenCL provides a uniform programming environment for
software developers to write efficient, portable code for high-performance compute servers, desktop computer systems and handheld devices using a diverse mix of multi-core CPUs, GPUs, Cell-type architectures and
other parallel processors such as DSPs.”
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OpenCL (II)
• Allows you to write C like code which executes on GPUs and many other devices
– CPUs, FPGAs, various other architectures
• Key point is data parallelism: applying the same function to a large amount of data
• Allows us to leverage devices like GPUs from Erlang easily with a minimal wrapper
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The Parallella Board
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Shiny prototype!
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The Parallella Board
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Epiphany Architecture
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Epiphany-IV 64-core 28nm (E64G401)
• 64 High Performance RISC CPU Cores • 800 MHz Operating Frequency • 100 GFLOPS Peak Performance • 1.6 TB/s Local Memory Bandwidth • 102 GB/s Network-On-Chip Bisection Bandwidth • 6.4 GB/s Off-Chip Bandwidth • 2 MB On-Chip Distributed Shared Memory • 2 Watt Maximum Chip Power Consumption • IEEE Floating Point Instruction Set • Fully-featured ANSI-C/C++ programmable • GNU/Eclipse based tool chain • Source synchronous LVDS off chip links for host or direct chip-to-
chip interfacing. • Chip to chip links for integrating up to 64 chips on a single board
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Parallella Vision Demo - Overview
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Parallella Vision Demo - Cameras
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Parallella Vision Demo - Architecture
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OpenCL and Erlang
• Erlang is not that great for crunching image data.
– This is where OpenCL fits in.
• Erlang provides an environment around OpenCL. Our server implementation collect frames, offloads processing to Epiphany and send results back.
– Low latency distributed communications and message passing between processes and nodes
– Monitoring and supervision facilities
– “Glue” between heterogeneous nodes
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OpenCL on the Parallella
• Parallella is a little different than standard GPUs
– Work sizes are different (smaller amount of cores compared to GPU)
– Requires some forethought into structuring your kernels
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Parallella and Erlang
• Ubuntu armhf packages up and running
– Will be included in the standard distro image
• Vision Demo code available now
– https://github.com/esl/parcv
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Embedded Landscape
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#include <stats.h>
Source: http://embedded.com/electronics-blogs/programming-pointers/4372180/Unexpected-trends
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External Interfaces in Erlang
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Accessing hardware
• Peripherals are memory mapped
• Access via /dev/mem…
– Faster, needs root, potentially dangerous!
• …or by kernel modules/sysfs
– Slower, doesn’t need root, easier, relatively safer
Generally very messy…
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Introducing…
Erlang/ALE
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http://github.com/esl/erlang-ale
Actor
Library for
Embedded
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Erlang/ALE
• Brings embedded peripheral interfaces into the Erlang domain
• Provides easy to use, familiar abstractions for Erlang programmers
• Uses Raspberry Pi as reference platform, easy to port it to other embedded platforms
• Open source (Apache version 2)
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Beta release
• Based on pihwm
– http://omerk.github.io/pihwm
• GPIO and GPIO interrupts, SPI, I2C and PWM peripherals supported
• Documentation, supporting material and educational package under development
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ALE Example: Blink!
{ok, _} = gpio:start_link(?LED_PIN, output),
blink() ->
gpio:write(?LED_PIN, 1),
timer:sleep(1000),
gpio:write(?LED_PIN, 0),
timer:sleep(1000).
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ALE Example: Interrupts
{ok, _} = gpio:start_link(?IN_PIN, input),
ok = gpio:set_int(?IN_PIN, rising),
handle_info({gpio_interrupt, _Pin, _Condition}, State) ->
blink().
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Hardware Projects – Demo Board
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Packages for Embedded Architectures
https://www.erlang-solutions.com/downloads/download-erlang-otp
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Erlang
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Thank you
• http://erlang-embedded.com
• embedded@erlang-solutions.com
• @ErlangEmbedded
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The world is concurrent. Things in the world don't share data. Things communicate with messages. Things fail.
- Joe Armstrong Father of Erlang
“
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