CPU-based Visualization Positions for Exascale Supercomputing

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

March 16, 2017

Editor’s Note: In this contributed perspective piece, Intel’s Jim Jeffers makes the case that CPU-based visualization is now widely adopted and as such is no longer a contrarian view, but is rather an exascale requirement. This article is a follow-on to a 2015 contribution from Jeffers and traces the progress for CPU-based software-defined visualization from that time.

Since our first formal product releases of OSPRay and OpenSWR libraries in 2016, CPU-based Software Defined Visualization (SDVis) has achieved wide-spread adoption. This rapid uptake is the result of two factors: (1) the general availability of highly-optimized CPU-based rendering software such as the open-source OSPRay ray tracing library and the high performance OpenSWR raster library in Mesa3d  integrated into popular visualization tools like Kitware’s Paraview and VTK, as well as the community tool, VisIt; and (2) SDVis filling the big data visualization community need for software that uses runtime visualization algorithms that can handle giga-scale and larger data.

These technologies aim to enable production visualization at scale on high performance computing resources, including supercomputers at Argonne National Laboratory, Los Alamos National Laboratory, the Texas Advanced Computing Center and many other facilities.

Award winning results, such as the Best Visualization and Data Analytics Showcase award won by the Los Alamos’ Data Science at Scale Team at Supercomputing 2016, highlight the fact that CPU-based rendering is now at the forefront of visualization technology. The LANL team’s award winning asteroid impact visualization is featured as an LANL newsroom picture of the week.

Figure 1: One image from the LANL asteroid impact video (Source: LANL)

Dr. Aaron Knoll (Research Scientist, Scientific Computing and Imaging Institute at the University of Utah) explains that the key change from last year lies in how much OSPRay and other SDVis CPU-based visualization libraries are now being used. “2016 is the year OSPRay became used in practice and production,” he said.

This trend has occurred throughout the scientific community. For example, four out of six finalists at Supercomputing 2016 used OSPRay and/or OpenSWR for their CPU-based visualizations. Of the remaining two finalists, one expressed interest in VMD rendering using OSPRay (now supported by that package), and the other used purely information visualization techniques outside the scope of OSPRay and SWR. Knoll also observed that about half of the non-finalists – at least 50 percent – used OSPRay or CPU-based visualization in some fashion. “Before,” he said, “people knew that OSPRay existed – now they just use it by default in production.”  So, unlike 2015, CPU-based visualizations are no longer a contrary view.

An exascale requirement

The idea behind SDVis is that larger data sets imply higher resolution (and therefore quality) that is too big for typical GPU memory. Focusing directly on the needs of large scale visualization rather than first targeting gaming means that SDVis software components can be designed to utilize massive-memory hardware and algorithms that scale as needed across the nodes in a cluster or inside a computational cloud.

Massive data poses a problem as it simply becomes impractical from a runtime point of view to move it around or keep multiple copies. It just takes too much time and memory capacity. This makes in-situ visualization (which minimizes data movement by running the visualization and simulation software on the same hardware) a “must-have.”  As I like to say, “A picture is worth an Exabyte”.

Eliminating data movement with in-situ visualization is a hot topic in the scientific literature and is now viewed by experts as a technology requirement for visualization in the exascale era. The paper “An Image-based Approach to Extreme Scale In Situ Visualization and Analysis” by James Ahrens et al. quantifies the data movement challenge as follows: “Imagery is on the order of 10**6 in size, whereas extreme scale simulation data is on the order of 10**15 in size.” Nine orders of magnitude is significant.

Ahrens explained, “We believe very strongly that in-situ is a requirement for exascale supercomputing.” More specifically, “For exascale, we need to be portable across all platforms. It’s an IO and memory capacity issue.” Knoll agrees that in-situ visualization is a requirement, “the old way of business has to change.”

Managing success: CPU-based SDVis robustly encompasses new algorithmic and software approaches

Dr. Knoll points out that in-situ visualization encompasses a spectrum of technologies, not just software alone. He references the 3D XPoint and Intel Omni-Path architecture. Jointly developed by Micron and Intel, 3D XPoint is a non-volatile storage media that can be used as storage or to augment main memory as the media is byte-addressable. Intel Omni-Path is a high-bandwidth, low-latency communications architecture created by Intel to increase performance and decrease cost.

“Memory is key,” Knoll stresses. He points out that, “An Intel Xeon Phi processor can support up to 24x more DRAM than an equivalent single GPU (NVIDIA Tesla P100 with 16 GB RAM), and an Intel Xeon workstations (e.g., the Brickland-EX platform with 6 TB) up to 384x more. With 3D XPoint the cost of this ‘memory’ will decrease substantially, which goes hand in hand with the benefits of big data runtime algorithms where it does not cost substantially more to access (and render) 6 TB or data than 16 GB of data.”

Knoll envisions 3D XPoint working as an in-core file-system at scale that blurs the line between RDMA, in-situ visualization, and distributed file-systems. One example is the CORAL project that, “leverages Intel Crystal Ridge [now known as 3D XPoint] non-volatile memory technology that is configured in DDR4 compatible DIMM form factor with processor load/store access semantics on CORAL point design compute nodes. This software design will allow applications running on any CORAL point design compute node to have a global view of and global access to Crystal Ridge that is on other compute nodes.”

“This technology gets me very excited,” Knoll says, noting the importance of the communications fabrics in making fast distributed memory a reality.

Focusing visualization solutions on data size rather than gaming usage means that SDVis software components can be designed to utilize massive-memory hardware and scale as needed across the nodes in a cluster or inside a computational cloud. This frees developers to design for the user rather than the hardware.

Figure 2: The Los Alamos team won the visualization award at SC16 for their SDVis based work

The transition from OpenGL targeted hardware rasterization to CPU-based rendering means that algorithm designers can exploit large memory (100’s of GBs or larger) visualization nodes to create logarithmic runtime algorithms.

Dr. Knoll stresses the importance of logarithmic runtime algorithms (a subtle but key technical point) as users are faced with orders of magnitude increases in data sizes on the big supercomputers. Logarithmic runtime algorithms are important for big visualizations and exascale computing as the runtime increases slowly (e.g. logarithmically) even when data sizes increase by orders of magnitude. Such algorithms tend to consume large amounts of in-core memory to hold the data and associated data structures. Thus memory capacity and latency are two key hardware metrics.

Research at the University of Utah [PDF] shows a single large memory (3 terabyte) workstation can deliver competitive and even superior interactive rendering performance compared to a 128-node GPU cluster; this is paradigm-changing. The group is exploring in-situ visualization using P-k-d trees and other fast, in-core approaches [PDF]. This project at the University of Utah showed that large “direct” in-core techniques are not only viable, but are at the bleeding edge of visualization research.

Figure 3: Uintah combustion simulation visualized with VisIt using OSPRay

Our design efforts on OSPRay includes the recognition that our software cannot – and does not – exist in a vacuum. The challenge is to provide sufficient modularity so researchers can adapt the package without having to touch the golden build source code. In other words, OSPRay is designed so researchers can explore new approaches without breaking the code for everyone. Our solution was to extend OSPRay with the aptly named ‘modules’ capability, which first appeared in v1.2.0. In using modules, the University of Utah team notes that modules provide a logical pairing between algorithm and data where researchers can: (1) write a module and (2) pair it with distributed parallel data processing and rendering API such as the Argonne vl3 volume rendering library. Ultimately, this can allow simpler workflows and more efficient visualization of specific large problems, such as materials and cosmology data. By design, successful and widely-utilized modules can be evaluated by the OSPRay team across a number of platforms as possible additions to the main body of the OSPRay code. Such accessibility and portability across CPU platforms highlights the adaptable yet robust characteristics of SDVis software.

Education will likely increase the rate of adoption

The adoption rate over the past year has been phenomenal, but we expect it to increase even further. As Dr. Knoll stated, “2016 is the year OSPRay became used in practice and production.” As a production visualization tool for scientific computing, OSPRay and more generically CPU-based SDVis has clearly come of age. Integration into packages such as ParaView and VisIt has made CPU-based rendering mainstream, which in turn means that using a CPU for visualization can no longer be considered a contrary viewpoint; it’s becoming the norm.

It is expected that education will likely increase the rate of adoption. A number of excellent educational resources are available online. For example, view the 2016 Intel HPC Developer software visualization track videos to delve more deeply into the technology and third-party use cases. Of course, hands-on experience and interacting with peers is always of value. Such interactions can be had at the IXPUG May 2017 Visualization workshop at the Texas Advanced Computing Center. Immediate hands-on experience can also be had simply by working with VisIt and ParaView, or downloading the OSPRay code from github and the OpenSWR code via the Mesa3D website.  Further background and up to date information about Software Defined Visualization is available at our IDZ (Intel Developer Zone) SDVis landing page., and in Chapter 17 of my Morgan Kaufman published book Intel Xeon Phi High Performance Programming: Knights Landing Edition.

To utilize CPU-based SDVis in your software, look to the following packages: (1) the OSPRay scalable, and portable ray tracing engine; (2) the Embree library of high-performance ray-tracing kernels; and (3) OpenSWR, a drop-in OpenGL replacement, highly scalable, CPU-based software rasterizer all provide core functionality for current SDV applications.

About the Author

Jim Jeffers is a Principal Engineer and engineering leader at Intel who is passionate about world changing technology as well as author and industry expert on parallel computing hardware.

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!

How the United States Invests in Supercomputing

November 14, 2018

The CORAL supercomputers Summit and Sierra are now the world's fastest computers and are already contributing to science with early applications. Ahead of SC18, Maciej Chojnowski with ICM at the University of Warsaw discussed the details of the CORAL project with Dr. Dimitri Kusnezov from the U.S. Department of Energy. Read more…

By Maciej Chojnowski

At SC18: Humanitarianism Amid Boom Times for HPC

November 14, 2018

At SC18 in Dallas, the feeling on the ground is one of forward-looking buoyancy. Like boom times that cycle through the Texas oil fields, the HPC industry is enjoying a prosperity seen only every few decades, one driven Read more…

By Doug Black

Nvidia’s Jensen Huang Delivers Vision for the New HPC

November 14, 2018

For nearly two hours on Monday at SC18, Jensen Huang, CEO of Nvidia, presented his expansive view of the future of HPC (and computing in general) as only he can do. Animated. Backstopped by a stream of data charts, produ Read more…

By John Russell

HPE Extreme Performance Solutions

AI Can Be Scary. But Choosing the Wrong Partners Can Be Mortifying!

As you continue to dive deeper into AI, you will discover it is more than just deep learning. AI is an extremely complex set of machine learning, deep learning, reinforcement, and analytics algorithms with varying compute, storage, memory, and communications needs. Read more…

IBM Accelerated Insights

New Data Management Techniques for Intelligent Simulations

The trend in high performance supercomputer design has evolved – from providing maximum compute capability for complex scalable science applications, to capacity computing utilizing efficient, cost-effective computing power for solving a small number of large problems or a large number of small problems. Read more…

New Panasas High Performance Storage Straddles Commercial-Traditional HPC

November 13, 2018

High performance storage vendor Panasas has launched a new version of its ActiveStor product line this morning featuring what the company said is the industry’s first plug-and-play, portable parallel file system that delivers up to 75 Gb/s per rack on industry standard hardware combined with “enterprise-grade reliability and manageability.” Read more…

By Doug Black

How the United States Invests in Supercomputing

November 14, 2018

The CORAL supercomputers Summit and Sierra are now the world's fastest computers and are already contributing to science with early applications. Ahead of SC18, Maciej Chojnowski with ICM at the University of Warsaw discussed the details of the CORAL project with Dr. Dimitri Kusnezov from the U.S. Department of Energy. Read more…

By Maciej Chojnowski

At SC18: Humanitarianism Amid Boom Times for HPC

November 14, 2018

At SC18 in Dallas, the feeling on the ground is one of forward-looking buoyancy. Like boom times that cycle through the Texas oil fields, the HPC industry is en Read more…

By Doug Black

Nvidia’s Jensen Huang Delivers Vision for the New HPC

November 14, 2018

For nearly two hours on Monday at SC18, Jensen Huang, CEO of Nvidia, presented his expansive view of the future of HPC (and computing in general) as only he can Read more…

By John Russell

New Panasas High Performance Storage Straddles Commercial-Traditional HPC

November 13, 2018

High performance storage vendor Panasas has launched a new version of its ActiveStor product line this morning featuring what the company said is the industry’s first plug-and-play, portable parallel file system that delivers up to 75 Gb/s per rack on industry standard hardware combined with “enterprise-grade reliability and manageability.” Read more…

By Doug Black

SC18 Student Cluster Competition – Revealing the Field

November 13, 2018

It’s November again and we’re almost ready for the kick-off of one of the greatest computer sports events in the world – the SC Student Cluster Competitio Read more…

By Dan Olds

US Leads Supercomputing with #1, #2 Systems & Petascale Arm

November 12, 2018

The 31st Supercomputing Conference (SC) - commemorating 30 years since the first Supercomputing in 1988 - kicked off in Dallas yesterday, taking over the Kay Ba Read more…

By Tiffany Trader

OpenACC Talks Up Summit and Community Momentum at SC18

November 12, 2018

OpenACC – the directives-based parallel programing model for optimizing applications on heterogeneous architectures – is showcasing user traction and HPC im Read more…

By John Russell

How ASCI Revolutionized the World of High-Performance Computing and Advanced Modeling and Simulation

November 9, 2018

The 1993 Supercomputing Conference was held in Portland, Oregon. That conference and it’s show floor provided a good snapshot of the uncertainty that U.S. supercomputing was facing in the early 1990s. Many of the companies exhibiting that year would soon be gone, either bankrupt or acquired by somebody else. Read more…

By Alex R. Larzelere

Cray Unveils Shasta, Lands NERSC-9 Contract

October 30, 2018

Cray revealed today the details of its next-gen supercomputing architecture, Shasta, selected to be the next flagship system at NERSC. We've known of the code-name "Shasta" since the Argonne slice of the CORAL project was announced in 2015 and although the details of that plan have changed considerably, Cray didn't slow down its timeline for Shasta. Read more…

By Tiffany Trader

TACC Wins Next NSF-funded Major Supercomputer

July 30, 2018

The Texas Advanced Computing Center (TACC) has won the next NSF-funded big supercomputer beating out rivals including the National Center for Supercomputing Ap Read more…

By John Russell

IBM at Hot Chips: What’s Next for Power

August 23, 2018

With processor, memory and networking technologies all racing to fill in for an ailing Moore’s law, the era of the heterogeneous datacenter is well underway, Read more…

By Tiffany Trader

Requiem for a Phi: Knights Landing Discontinued

July 25, 2018

On Monday, Intel made public its end of life strategy for the Knights Landing "KNL" Phi product set. The announcement makes official what has already been wide Read more…

By Tiffany Trader

House Passes $1.275B National Quantum Initiative

September 17, 2018

Last Thursday the U.S. House of Representatives passed the National Quantum Initiative Act (NQIA) intended to accelerate quantum computing research and developm Read more…

By John Russell

CERN Project Sees Orders-of-Magnitude Speedup with AI Approach

August 14, 2018

An award-winning effort at CERN has demonstrated potential to significantly change how the physics based modeling and simulation communities view machine learni Read more…

By Rob Farber

Summit Supercomputer is Already Making its Mark on Science

September 20, 2018

Summit, now the fastest supercomputer in the world, is quickly making its mark in science – five of the six finalists just announced for the prestigious 2018 Read more…

By John Russell

New Deep Learning Algorithm Solves Rubik’s Cube

July 25, 2018

Solving (and attempting to solve) Rubik’s Cube has delighted millions of puzzle lovers since 1974 when the cube was invented by Hungarian sculptor and archite Read more…

By John Russell

Leading Solution Providers

US Leads Supercomputing with #1, #2 Systems & Petascale Arm

November 12, 2018

The 31st Supercomputing Conference (SC) - commemorating 30 years since the first Supercomputing in 1988 - kicked off in Dallas yesterday, taking over the Kay Ba Read more…

By Tiffany Trader

TACC’s ‘Frontera’ Supercomputer Expands Horizon for Extreme-Scale Science

August 29, 2018

The National Science Foundation and the Texas Advanced Computing Center announced today that a new system, called Frontera, will overtake Stampede 2 as the fast Read more…

By Tiffany Trader

HPE No. 1, IBM Surges, in ‘Bucking Bronco’ High Performance Server Market

September 27, 2018

Riding healthy U.S. and global economies, strong demand for AI-capable hardware and other tailwind trends, the high performance computing server market jumped 28 percent in the second quarter 2018 to $3.7 billion, up from $2.9 billion for the same period last year, according to industry analyst firm Hyperion Research. Read more…

By Doug Black

Intel Announces Cooper Lake, Advances AI Strategy

August 9, 2018

Intel's chief datacenter exec Navin Shenoy kicked off the company's Data-Centric Innovation Summit Wednesday, the day-long program devoted to Intel's datacenter Read more…

By Tiffany Trader

Germany Celebrates Launch of Two Fastest Supercomputers

September 26, 2018

The new high-performance computer SuperMUC-NG at the Leibniz Supercomputing Center (LRZ) in Garching is the fastest computer in Germany and one of the fastest i Read more…

By Tiffany Trader

Houston to Field Massive, ‘Geophysically Configured’ Cloud Supercomputer

October 11, 2018

Based on some news stories out today, one might get the impression that the next system to crack number one on the Top500 would be an industrial oil and gas mon Read more…

By Tiffany Trader

MLPerf – Will New Machine Learning Benchmark Help Propel AI Forward?

May 2, 2018

Let the AI benchmarking wars begin. Today, a diverse group from academia and industry – Google, Baidu, Intel, AMD, Harvard, and Stanford among them – releas Read more…

By John Russell

Google Releases Machine Learning “What-If” Analysis Tool

September 12, 2018

Training machine learning models has long been time-consuming process. Yesterday, Google released a “What-If Tool” for probing how data point changes affect a model’s prediction. The new tool is being launched as a new feature of the open source TensorBoard web application... Read more…

By John Russell

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