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!

A Beginner’s Guide to the ASC19 Finals

April 22, 2019

Three thousand watts. That's how much power the competitors in the 2019 ASC Student Supercomputer Challenge here in Dalian, China, have to work with. Everybody would like more juice to run compute-intensive HPC simulatio Read more…

By Alex Woodie

Is Data Science the Fourth Pillar of the Scientific Method?

April 18, 2019

Nvidia CEO Jensen Huang revived a decade-old debate last month when he said that modern data science (AI plus HPC) has become the fourth pillar of the scientific method. While some disagree with the notion that statistic Read more…

By Alex Woodie

At ASF 2019: The Virtuous Circle of Big Data, AI and HPC

April 18, 2019

We've entered a new phase in IT -- in the world, really -- where the combination of big data, artificial intelligence, and high performance computing is pushing the bounds of what's possible in business and science, in w Read more…

By Alex Woodie with Doug Black and Tiffany Trader

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

Bridging HPC and Cloud Native Development with Kubernetes

The HPC community has historically developed its own specialized software stack including schedulers, filesystems, developer tools, container technologies tuned for performance and large-scale on-premises deployments. Read more…

Google Open Sources TensorFlow Version of MorphNet DL Tool

April 18, 2019

Designing optimum deep neural networks remains a non-trivial exercise. “Given the large search space of possible architectures, designing a network from scratch for your specific application can be prohibitively expens Read more…

By John Russell

A Beginner’s Guide to the ASC19 Finals

April 22, 2019

Three thousand watts. That's how much power the competitors in the 2019 ASC Student Supercomputer Challenge here in Dalian, China, have to work with. Everybody Read more…

By Alex Woodie

At ASF 2019: The Virtuous Circle of Big Data, AI and HPC

April 18, 2019

We've entered a new phase in IT -- in the world, really -- where the combination of big data, artificial intelligence, and high performance computing is pushing Read more…

By Alex Woodie with Doug Black and Tiffany Trader

Interview with 2019 Person to Watch Michela Taufer

April 18, 2019

Today, as part of our ongoing HPCwire People to Watch focus series, we are highlighting our interview with 2019 Person to Watch Michela Taufer. Michela -- the Read more…

By HPCwire Editorial Team

Intel Gold U-Series SKUs Reveal Single Socket Intentions

April 18, 2019

Intel plans to jump into the single socket market with a portion of its just announced Cascade Lake microprocessor line according to one media report. This isn Read more…

By John Russell

BSC Researchers Shrink Floating Point Formats to Accelerate Deep Neural Network Training

April 15, 2019

Sometimes calculating solutions as precisely as a computer can wastes more CPU resources than is necessary. A case in point is with deep learning. In early stag Read more…

By Ken Strandberg

Intel Extends FPGA Ecosystem with 10nm Agilex

April 11, 2019

The insatiable appetite for higher throughput and lower latency – particularly where edge analytics and AI, network functions, or for a range of datacenter ac Read more…

By Doug Black

Nvidia Doubles Down on Medical AI

April 9, 2019

Nvidia is collaborating with medical groups to push GPU-powered AI tools into clinical settings, including radiology and drug discovery. The GPU leader said Monday it will collaborate with the American College of Radiology (ACR) to provide clinicians with its Clara AI tool kit. The partnership would allow radiologists to leverage AI techniques for diagnostic imaging using their own clinical data. Read more…

By George Leopold

Digging into MLPerf Benchmark Suite to Inform AI Infrastructure Decisions

April 9, 2019

With machine learning and deep learning storming into the datacenter, the new challenge is optimizing infrastructure choices to support diverse ML and DL workfl 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

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

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

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

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

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

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

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

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

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

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

Intel Launches Cascade Lake Xeons with Up to 56 Cores

April 2, 2019

At Intel's Data-Centric Innovation Day in San Francisco (April 2), the company unveiled its second-generation Xeon Scalable (Cascade Lake) family and debuted it Read more…

By Tiffany Trader

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

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

Intel Extends FPGA Ecosystem with 10nm Agilex

April 11, 2019

The insatiable appetite for higher throughput and lower latency – particularly where edge analytics and AI, network functions, or for a range of datacenter ac Read more…

By Doug Black

UC Berkeley Paper Heralds Rise of Serverless Computing in the Cloud – Do You Agree?

February 13, 2019

Almost exactly ten years to the day from publishing of their widely-read, seminal paper on cloud computing, UC Berkeley researchers have issued another ambitious examination of cloud computing - Cloud Programming Simplified: A Berkeley View on Serverless Computing. The new work heralds the rise of ‘serverless computing’ as the next dominant phase of cloud computing. 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