OpenCL: Free Your GPU… and the rest of your system too!

By Nicole Hemsoth

May 6, 2013

For the uninitiated, OpenCL may just seem like a tool for accessing GPUs in a portable manner, an alternative to CUDA that works for ATI cards.  This view of OpenCL is a vast oversimplification.  The truth is that OpenCL offers a rich set of features that will provide the infrastructure needed for application developers to fully utilize an increasingly heterogeneous hardware future.  In this article, we will consider how OpenCL can address some of the coming challenges, and what the OpenCL community is doing to insure that there is a vibrant ecosystem of support, as more and more users turn to OpenCL from vendor-locked solutions.

By now, we’ve all heard of the power wall, and we know that the only way to climb over it is parallelism.  Data-level parallelism (think GPUs) was the first technology to make a mark in the new computing age.  Employing very uniform work units, data-parallel implementations have achieved noteworthy performance gains for many scientific algorithms, and OpenCL certainly has support for this model.

However, this is not the end of the story.  As CPU vendors seek to adapt their architectures to the new parallel reality (and GPU vendors evolve theirs), alternate ideas about how to expose and exploit parallelism within a program are surfacing.  These designs seek to mitigate performance limitations for algorithms where the majority of the work does not fit neatly into non-divergent units of execution.

One such approach is to take a multicore architecture and scale it, providing increased resources for task-parallel workloads.  An example of this is the Intel Xeon Phi architecture, released in 2012.  Sharing similarities with CPUs and GPUs, the Xeon Phi programming model requires a little bit of both.  Like a CPU, the Xeon Phi prefers somewhat longer persistence from a work item, so that its caching scheme can work efficiently to hide memory access latency.  At the same time, the Xeon Phi offers many more units of execution than traditional SMP implementations are designed to handle.  For many applications, there is likely a good balance to be found that combines both data and task-parallelism, and fortunately for developers, Intel has provided OpenCL support for their entire processor line, including Xeon Phi and embedded graphics.  As an example of why this is beneficial, OpenCL has no problem expressing local accumulations, with or without blocked memory access, and its interface for managing work groups is sufficient for any level of data-parallel execution.  These features make it possible to seamlessly integrate mixed programming models that reflect the hybrid characteristics of the Xeon Phi hardware.

A third hardware strategy that has already been widely deployed, but not yet fully utilized (and often not even recognized) is the heterogeneous system-on-a-chip (SoC).  Founded with the now-extinct IBM Cell, and combining so-called latency-optimized cores (think CPU) with throughput-optimized cores (GPU), heterogeneous architectures attempt to provide the best of both worlds, albeit on separate parts of the die.  If you are reading this article on a mobile computing device, or a late model laptop or desktop, chances are that you already have a heterogeneous chip at your disposal: Apple A Series, NVIDIA Tegra, AMD Fusion, Qualcomm Snapdragon, and Intel Ivybridge are all heterogeneous.  Some of these chips are even likely to find their way onto the next generation of supercomputers.  If this is the case, and unless you are explicitly utilizing these resources, they can be an energy drain on your system with no benefit.  OpenCL makes the full potential of heterogeneous architectures available to the developer.

A final evolving hardware strategy that should be mentioned is the Field Programmable Gate Array (FPGA).  Few general-purpose processing architectures can compete with the energy efficiency of a custom circuit.  Sadly, very few developers are prepared to invest the time and effort needed to learn VHDL or Verilog to turn their ideas into a working core.  One very novel application of OpenCL seeks to remove this barrier by creating FPGA firmware from kernels written in the OpenCL C programming language.  The Altera Corporation (maker of the Stratix V architecture) has released the Altera SDK for OpenCL.  Using this set of tools, developers can generate custom hardware, tailor-made for their specific application.  This frees the application developer from the tedium of a job better left to electrical engineers, and, at the same time, opens a fascinating world of possibilities.

All of the hardware trends we have discussed, taken together, present significant challenges for any vendor-specific toolchain.  Consider the programming model for a system with a heterogeneous host processor, augmented with a discrete accelerator (GPU or other).  Efficient utilization of these resources will require the ability to tightly integrate both data-parallel and task-parallel work, along with data motion between the various compute units.  Do we really want to rely on a clumsy vendor-specific patchwork of tools to meet this challenge?  Imagine now that you must also support a similarly provisioned system but built from a different set of architectures.  How many versions of your code would you have to write?  How many man-years might you need for such a task?

With OpenCL, implementing such a model is straightforward.  This is because OpenCL is a natural reflection of the competing hardware architectures on which it is designed to run.  OpenCL accommodates each of the above architectures, and, by design, has no trouble in accommodating all of them together.  Being an open standard, new vendors are able to join the OpenCL dialogue, insuring that OpenCL will be able to support future hardware developments, regardless of what direction they may take.  Additionally, with its support for custom devices, OpenCL offers the flexibility to incorporate system resources that do not fit the current notion of a compute device, opening opportunities for integration of DSPs or internally developed ASICs.

Much of the FUD (fear, uncertainty and doubt) directed at OpenCL has focused on the mantra that OpenCL may be portable but not portably performant.  This is an attempt to divert the discussion from the real issue.  Clearly, all hardware is not the same, and no single approach to expressing a parallel algorithm can be optimal across the wide variety of architectures that are currently available.  This is not the point of OpenCL.  The true power of OpenCL is that it provides the infrastructure that allows us to target different combinations of hardware within a single framework.  OpenCL frees us from the old scenario where developers must use an ad hoc combination of interfaces to support multiple architectures.  True, we may need to write a new version of our kernel to get the best performance on Architecture A, but isn’t this what we actually want?  With OpenCL, developers can easily switch between different kernel implementations, execution topologies and memory model interpretations.  This flexibility is at the core of the OpenCL philosophy.  So, to the OpenCL detractors: A toad may criticize the way that a bird flaps its wings, but only to distract us from the fact that the toad cannot fly.

One thing that has thus far been missing from OpenCL is a formal organization for its users.  To remedy this, some of the primary advocates of OpenCL have organized a new user group.  Since OpenCL is about computing portability, this new group is aptly named Comportability.  Established as a non-profit corporation, Comportability is an open forum for all things related to OpenCL.  Membership is free.  Individuals are invited to participate in discussions, upload software and take part in the annual workshop.  Institutions and vendors may join as voting members, and are invited to form regional chapters, which are encouraged to host future meetings and workshops.

The 1st International Workshop on OpenCL (IWOCL), the annual meeting of the Comportability user group will take place May 13-14, 2013 at the Technology Square Research Building (TSRB) at the Georgia Institute of Technology in Atlanta, Georgia.  Registration for this event is open and the program is on-line.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

GTC 2019: Chief Scientist Bill Dally Provides Glimpse into Nvidia Research Engine

March 22, 2019

Amid the frenzy of GTC this week – Nvidia’s annual conference showcasing all things GPU (and now AI) – William Dally, chief scientist and SVP of research, provided a brief but insightful portrait of Nvidia’s rese Read more…

By John Russell

ORNL Helps Identify Challenges of Extremely Heterogeneous Architectures

March 21, 2019

Exponential growth in classical computing over the last two decades has produced hardware and software that support lightning-fast processing speeds, but advancements are topping out as computing architectures reach thei Read more…

By Laurie Varma

Interview with 2019 Person to Watch Jim Keller

March 21, 2019

On the heels of Intel's reaffirmation that it will deliver the first U.S. exascale computer in 2021, which will feature the company's new Intel Xe architecture, we bring you our interview with our 2019 Person to Watch Jim Keller, head of the Silicon Engineering Group at Intel. Read more…

By HPCwire Editorial Team

HPE Extreme Performance Solutions

HPE and Intel® Omni-Path Architecture: How to Power a Cloud

Learn how HPE and Intel® Omni-Path Architecture provide critical infrastructure for leading Nordic HPC provider’s HPCFLOW cloud service.

powercloud_blog.jpgFor decades, HPE has been at the forefront of high-performance computing, and we’ve powered some of the fastest and most robust supercomputers in the world. Read more…

IBM Accelerated Insights

Insurance: Where’s the Risk?

Insurers are facing extreme competitive challenges in their core businesses. Property and Casualty (P&C) and Life and Health (L&H) firms alike are highly impacted by the ongoing globalization, increasing regulation, and digital transformation of their client bases. Read more…

What’s New in HPC Research: TensorFlow, Buddy Compression, Intel Optane & More

March 20, 2019

In this bimonthly feature, HPCwire highlights newly published research in the high-performance computing community and related domains. From parallel programming to exascale to quantum computing, the details are here. Read more…

By Oliver Peckham

GTC 2019: Chief Scientist Bill Dally Provides Glimpse into Nvidia Research Engine

March 22, 2019

Amid the frenzy of GTC this week – Nvidia’s annual conference showcasing all things GPU (and now AI) – William Dally, chief scientist and SVP of research, Read more…

By John Russell

At GTC: Nvidia Expands Scope of Its AI and Datacenter Ecosystem

March 19, 2019

In the high-stakes race to provide the AI life-cycle solution of choice, three of the biggest horses in the field are IBM, Intel and Nvidia. While the latter is only a fraction of the size of its two bigger rivals, and has been in business for only a fraction of the time, Nvidia continues to impress with an expanding array of new GPU-based hardware, software, robotics, partnerships and... Read more…

By Doug Black

Nvidia Debuts Clara AI Toolkit with Pre-Trained Models for Radiology Use

March 19, 2019

AI’s push into healthcare got a boost yesterday with Nvidia’s release of the Clara Deploy AI toolkit which includes 13 pre-trained models for use in radiolo Read more…

By John Russell

It’s Official: Aurora on Track to Be First US Exascale Computer in 2021

March 18, 2019

The U.S. Department of Energy along with Intel and Cray confirmed today that an Intel/Cray supercomputer, "Aurora," capable of sustained performance of one exaf Read more…

By Tiffany Trader

Why Nvidia Bought Mellanox: ‘Future Datacenters Will Be…Like High Performance Computers’

March 14, 2019

“Future datacenters of all kinds will be built like high performance computers,” said Nvidia CEO Jensen Huang during a phone briefing on Monday after Nvidia revealed scooping up the high performance networking company Mellanox for $6.9 billion. Read more…

By Tiffany Trader

Oil and Gas Supercloud Clears Out Remaining Knights Landing Inventory: All 38,000 Wafers

March 13, 2019

The McCloud HPC service being built by Australia’s DownUnder GeoSolutions (DUG) outside Houston is set to become the largest oil and gas cloud in the world th Read more…

By Tiffany Trader

Quick Take: Trump’s 2020 Budget Spares DoE-funded HPC but Slams NSF and NIH

March 12, 2019

U.S. President Donald Trump’s 2020 budget request, released yesterday, proposes deep cuts in many science programs but seems to spare HPC funding by the Depar Read more…

By John Russell

Nvidia Wins Mellanox Stakes for $6.9 Billion

March 11, 2019

The long-rumored acquisition of Mellanox came to fruition this morning with GPU chipmaker Nvidia’s announcement that it has purchased the high-performance net Read more…

By Doug Black

Quantum Computing Will Never Work

November 27, 2018

Amid the gush of money and enthusiastic predictions being thrown at quantum computing comes a proposed cold shower in the form of an essay by physicist Mikhail Read more…

By John Russell

The Case Against ‘The Case Against Quantum Computing’

January 9, 2019

It’s not easy to be a physicist. Richard Feynman (basically the Jimi Hendrix of physicists) once said: “The first principle is that you must not fool yourse Read more…

By Ben Criger

ClusterVision in Bankruptcy, Fate Uncertain

February 13, 2019

ClusterVision, European HPC specialists that have built and installed over 20 Top500-ranked systems in their nearly 17-year history, appear to be in the midst o Read more…

By Tiffany Trader

Why Nvidia Bought Mellanox: ‘Future Datacenters Will Be…Like High Performance Computers’

March 14, 2019

“Future datacenters of all kinds will be built like high performance computers,” said Nvidia CEO Jensen Huang during a phone briefing on Monday after Nvidia revealed scooping up the high performance networking company Mellanox for $6.9 billion. Read more…

By Tiffany Trader

Intel Reportedly in $6B Bid for Mellanox

January 30, 2019

The latest rumors and reports around an acquisition of Mellanox focus on Intel, which has reportedly offered a $6 billion bid for the high performance interconn Read more…

By Doug Black

Looking for Light Reading? NSF-backed ‘Comic Books’ Tackle Quantum Computing

January 28, 2019

Still baffled by quantum computing? How about turning to comic books (graphic novels for the well-read among you) for some clarity and a little humor on QC. The Read more…

By John Russell

Contract Signed for New Finnish Supercomputer

December 13, 2018

After the official contract signing yesterday, configuration details were made public for the new BullSequana system that the Finnish IT Center for Science (CSC Read more…

By Tiffany Trader

It’s Official: Aurora on Track to Be First US Exascale Computer in 2021

March 18, 2019

The U.S. Department of Energy along with Intel and Cray confirmed today that an Intel/Cray supercomputer, "Aurora," capable of sustained performance of one exaf Read more…

By Tiffany Trader

Leading Solution Providers

SC 18 Virtual Booth Video Tour

Advania @ SC18 AMD @ SC18
ASRock Rack @ SC18
DDN Storage @ SC18
HPE @ SC18
IBM @ SC18
Lenovo @ SC18 Mellanox Technologies @ SC18
NVIDIA @ SC18
One Stop Systems @ SC18
Oracle @ SC18 Panasas @ SC18
Supermicro @ SC18 SUSE @ SC18 TYAN @ SC18
Verne Global @ SC18

Deep500: ETH Researchers Introduce New Deep Learning Benchmark for HPC

February 5, 2019

ETH researchers have developed a new deep learning benchmarking environment – Deep500 – they say is “the first distributed and reproducible benchmarking s Read more…

By John Russell

IBM Quantum Update: Q System One Launch, New Collaborators, and QC Center Plans

January 10, 2019

IBM made three significant quantum computing announcements at CES this week. One was introduction of IBM Q System One; it’s really the integration of IBM’s Read more…

By John Russell

IBM Bets $2B Seeking 1000X AI Hardware Performance Boost

February 7, 2019

For now, AI systems are mostly machine learning-based and “narrow” – powerful as they are by today's standards, they're limited to performing a few, narro Read more…

By Doug Black

The Deep500 – Researchers Tackle an HPC Benchmark for Deep Learning

January 7, 2019

How do you know if an HPC system, particularly a larger-scale system, is well-suited for deep learning workloads? Today, that’s not an easy question to answer Read more…

By John Russell

HPC Reflections and (Mostly Hopeful) Predictions

December 19, 2018

So much ‘spaghetti’ gets tossed on walls by the technology community (vendors and researchers) to see what sticks that it is often difficult to peer through Read more…

By John Russell

Arm Unveils Neoverse N1 Platform with up to 128-Cores

February 20, 2019

Following on its Neoverse roadmap announcement last October, Arm today revealed its next-gen Neoverse microarchitecture with compute and throughput-optimized si Read more…

By Tiffany Trader

Move Over Lustre & Spectrum Scale – Here Comes BeeGFS?

November 26, 2018

Is BeeGFS – the parallel file system with European roots – on a path to compete with Lustre and Spectrum Scale worldwide in HPC environments? Frank Herold Read more…

By John Russell

France to Deploy AI-Focused Supercomputer: Jean Zay

January 22, 2019

HPE announced today that it won the contract to build a supercomputer that will drive France’s AI and HPC efforts. The computer will be part of GENCI, the Fre Read more…

By Tiffany Trader

  • arrow
  • Click Here for More Headlines
  • arrow
Do NOT follow this link or you will be banned from the site!
Share This