The Future of Workload Management

By Chad Harrington

October 12, 2012

The essayist Paul Valery once quipped, “The trouble with our times is that the future is not what it used to be.” Surely, there is truth in that. The future of workload management continues to evolve; it is definitely not what it used to be.

As we look toward the future of workload management, we see three major trends: application insight, big data awareness, and HPC clouds. The trends are inter-related and we’ll discuss each in turn.

Application Insight

First, workload managers need to have greater insight into the applications they run. The more deeply the workload manager can understand the workload, the more efficiently it can schedule, manage, and adapt the computing environment. Today’s workload managers understand basic workload requirements and can track an application’s progress. However, there is more that can be done. In the future, we’ll see more emphasis on understanding an application’s purpose and key metrics. If the workload manager understands the application’s current and future needs, it can make much more optimal decisions. Metrics such as I/O bandwidth, memory allocation, storage space, CPU and GPU cycles, etc., all help the workload manager understand an application in order to optimally manage it.

Application-specific metrics, such as simulations per second, genes matched per second, etc., are more important than generic CPU and memory metrics. They best describe an application’s performance. By monitoring these application-specific metrics, the workload manager can understand how system-level variables impact application performance. For instance, an application-aware workload manager could observe that a particular application’s performance degrades substantially when it runs at the same time as a another specific application. Armed with this data, the workload manager can make sure those two conflicting applications do not run at the same time.

Big Data Awareness

Closely related to the application insight trend, we see increasing demand for big data awareness. Modern scientific computing operates on massive amounts of data, far more than ever before. Managing this flood of data is difficult; the future of workload management depends on being able to efficiently manage it.

Specifically, big data applications require I/O performance that is appropriate to the application. With multiple applications running simultaneously in a cluster, the workload manager needs to understand and satisfy the I/O needs of each application. A Big Data-aware workload manager will be able to schedule the various applications, such that their I/O demands do not conflict, ensuring that the required storage performance is available when it is needed.

Workload managers of the future will integrate directly with the storage management system. This will allow the workload manager to control the I/O allocation of each application, ensuring that no application monopolizes the I/O bandwidth. When multiple applications are contending for the same physical disk drive, the drive head thrashes between servicing each of the conflicting requests. This data contention can cause a 100-fold decrease in performance. With the workload manager directly managing the storage system, it can remove this thrashing and greatly increase application performance.

I/O performance also heavily depends on data locality. Generally speaking, today’s workload managers treat data as blobs of raw bytes, to be shuffled about with little understanding of their content. In the future, workload managers will increasingly understand the data’s structure and attributes. For example, a future workload manager could understand that a particular application uses structured data made up of small records which are randomly accessed. The workload manager could then allocate more I/O operations per second to that application than to a traditional batch-processing application, which reads sequentially from the disk. By understanding the different I/O needs of the different applications, the workload manager can exploit those factors in their scheduling decisions.

Virtualization and HPC Cloud

Lastly, we predict the continued rise of virtualization and HPC clouds. This is perhaps the biggest future trend for workload management. Historically, virtualization was anathema to high performance computing practitioners. The so-called virtualization tax, or performance penalty caused by virtualization, was too high a price to pay for high-performance workloads.

However, in recent years, this penalty has decreased to the point of being almost negligible for many applications. This trend, combined with virtualization’s greatly increased flexibility, has made virtualization a growing tool in the HPC arsenal. Virtual machines can be easily started, stopped, moved, stored, and altered, and are easier for the workload manager to schedule and control. This increased flexibility results in higher overall system utilization and greater return on investment. As a result, more and more HPC sites are adopting virtualization for a wider variety of workloads.

Taking virtualization to the next level, HPC clouds combine automated machine provisioning with workload management technologies, pay-per-use cost models, and self-service job submission. Instead of manually provisioning nodes for new compute jobs, an HPC cloud automatically provisions the appropriate environment as needed, based on the jobs submitted. These technologies work together to lower costs and increase system utilization.

HPC clouds can be public or private. Public clouds are operated by a third party who provides computing services to the public. Private clouds are operated by a particular HPC site for their own use, typically using hardware they own. Private clouds provide flexibility and cost advantages of the cloud model while still providing the security and control that many HPC users prefer.

HPC clouds increase the accessibility and flexibility of HPC systems. This brings HPC to a wider audience and lowers the overall cost of HPC. As more users take advantage of HPC, the demands become more varied. Tomorrow’s workload managers will have to cope with these realities, dealing with more users and a wider variety of workloads, both physical and virtual.

The Future Is Not Static

As Valery intimated, the future is not static. As our world changes, the trends that drive the future change with it. Application awareness, big data, and HPC clouds are changing how we do scientific computing. Workload managers must continue to evolve along with these trends.

About the Author

Chad Harrington manages Adaptive Computing‘s worldwide marketing efforts. Prior to Adaptive, Chad was a strategy consultant, helping companies increase shareholder value. Previously, he was CEO and founder of DataScaler, a database technology company which Oracle acquired in 2010. He has a history of success, holding executive, marketing, and business development roles at companies that were acquired by Symantec, McAfee, Check Point, and Oracle. As an information technology veteran, Chad speaks at industry conferences and in the media about technology trends such as cloud computing, data center architecture, security, and the future of computing. He has appeared on CNN, Marketwatch, Univision, and in other major media outlets. Chad holds a Computer Engineering degree from Brigham Young University.

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!

Fluid HPC: How Extreme-Scale Computing Should Respond to Meltdown and Spectre

February 15, 2018

The Meltdown and Spectre vulnerabilities are proving difficult to fix, and initial experiments suggest security patches will cause significant performance penalties to HPC applications. Even as these patches are rolled o Read more…

By Pete Beckman

Intel Touts Silicon Spin Qubits for Quantum Computing

February 14, 2018

Debate around what makes a good qubit and how best to manufacture them is a sprawling topic. There are many insistent voices favoring one or another approach. Referencing a paper published today in Nature, Intel has offe Read more…

By John Russell

Brookhaven Ramps Up Computing for National Security Effort

February 14, 2018

Last week, Dan Coats, the director of Director of National Intelligence for the U.S., warned the Senate Intelligence Committee that Russia was likely to meddle in the 2018 mid-term U.S. elections, much as it stands accused of doing in the 2016 Presidential election. Read more…

By John Russell

HPE Extreme Performance Solutions

Safeguard Your HPC Environment with the World’s Most Secure Industry Standard Servers

Today’s organizations operate in an environment with ever-evolving threats, and in order to protect themselves they must continuously bolster their security strategy. Hewlett Packard Enterprise (HPE) and Intel® are addressing modern security challenges with the world’s most secure industry standard servers powered by the latest generation of Intel® Xeon® Scalable processors. Read more…

AI Cloud Competition Heats Up: Google’s TPUs, Amazon Building AI Chip

February 12, 2018

Competition in the white hot AI (and public cloud) market pits Google against Amazon this week, with Google offering AI hardware on its cloud platform intended to make it easier, faster and cheaper to train and run machi Read more…

By Doug Black

Fluid HPC: How Extreme-Scale Computing Should Respond to Meltdown and Spectre

February 15, 2018

The Meltdown and Spectre vulnerabilities are proving difficult to fix, and initial experiments suggest security patches will cause significant performance penal Read more…

By Pete Beckman

Brookhaven Ramps Up Computing for National Security Effort

February 14, 2018

Last week, Dan Coats, the director of Director of National Intelligence for the U.S., warned the Senate Intelligence Committee that Russia was likely to meddle in the 2018 mid-term U.S. elections, much as it stands accused of doing in the 2016 Presidential election. Read more…

By John Russell

AI Cloud Competition Heats Up: Google’s TPUs, Amazon Building AI Chip

February 12, 2018

Competition in the white hot AI (and public cloud) market pits Google against Amazon this week, with Google offering AI hardware on its cloud platform intended Read more…

By Doug Black

Russian Nuclear Engineers Caught Cryptomining on Lab Supercomputer

February 12, 2018

Nuclear scientists working at the All-Russian Research Institute of Experimental Physics (RFNC-VNIIEF) have been arrested for using lab supercomputing resources to mine crypto-currency, according to a report in Russia’s Interfax News Agency. Read more…

By Tiffany Trader

The Food Industry’s Next Journey — from Mars to Exascale

February 12, 2018

Global food producer and one of the world's leading chocolate companies Mars Inc. has a unique perspective on the impact that exascale computing will have on the food industry. Read more…

By Scott Gibson, Oak Ridge National Laboratory

Singularity HPC Container Start-Up – Sylabs – Emerges from Stealth

February 8, 2018

The driving force behind Singularity, the popular HPC container technology, is bringing the open source platform to the enterprise with the launch of a new vent Read more…

By George Leopold

Dell EMC Debuts PowerEdge Servers with AMD EPYC Chips

February 6, 2018

AMD notched another EPYC processor win today with Dell EMC’s introduction of three PowerEdge servers (R6415, R7415, and R7425) based on the EPYC 7000-series p Read more…

By John Russell

‘Next Generation’ Universe Simulation Is Most Advanced Yet

February 5, 2018

The research group that gave us the most detailed time-lapse simulation of the universe’s evolution in 2014, spanning 13.8 billion years of cosmic evolution, is back in the spotlight with an even more advanced cosmological model that is providing new insights into how black holes influence the distribution of dark matter, how heavy elements are produced and distributed, and where magnetic fields originate. Read more…

By Tiffany Trader

Inventor Claims to Have Solved Floating Point Error Problem

January 17, 2018

"The decades-old floating point error problem has been solved," proclaims a press release from inventor Alan Jorgensen. The computer scientist has filed for and Read more…

By Tiffany Trader

Japan Unveils Quantum Neural Network

November 22, 2017

The U.S. and China are leading the race toward productive quantum computing, but it's early enough that ultimate leadership is still something of an open questi Read more…

By Tiffany Trader

AMD Showcases Growing Portfolio of EPYC and Radeon-based Systems at SC17

November 13, 2017

AMD’s charge back into HPC and the datacenter is on full display at SC17. Having launched the EPYC processor line in June along with its MI25 GPU the focus he Read more…

By John Russell

Researchers Measure Impact of ‘Meltdown’ and ‘Spectre’ Patches on HPC Workloads

January 17, 2018

Computer scientists from the Center for Computational Research, State University of New York (SUNY), University at Buffalo have examined the effect of Meltdown Read more…

By Tiffany Trader

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

IBM Begins Power9 Rollout with Backing from DOE, Google

December 6, 2017

After over a year of buildup, IBM is unveiling its first Power9 system based on the same architecture as the Department of Energy CORAL supercomputers, Summit a Read more…

By Tiffany Trader

Fast Forward: Five HPC Predictions for 2018

December 21, 2017

What’s on your list of high (and low) lights for 2017? Volta 100’s arrival on the heels of the P100? Appearance, albeit late in the year, of IBM’s Power9? Read more…

By John Russell

Russian Nuclear Engineers Caught Cryptomining on Lab Supercomputer

February 12, 2018

Nuclear scientists working at the All-Russian Research Institute of Experimental Physics (RFNC-VNIIEF) have been arrested for using lab supercomputing resources to mine crypto-currency, according to a report in Russia’s Interfax News Agency. Read more…

By Tiffany Trader

Leading Solution Providers

Chip Flaws ‘Meltdown’ and ‘Spectre’ Loom Large

January 4, 2018

The HPC and wider tech community have been abuzz this week over the discovery of critical design flaws that impact virtually all contemporary microprocessors. T Read more…

By Tiffany Trader

Perspective: What Really Happened at SC17?

November 22, 2017

SC is over. Now comes the myriad of follow-ups. Inboxes are filled with templated emails from vendors and other exhibitors hoping to win a place in the post-SC thinking of booth visitors. Attendees of tutorials, workshops and other technical sessions will be inundated with requests for feedback. Read more…

By Andrew Jones

How Meltdown and Spectre Patches Will Affect HPC Workloads

January 10, 2018

There have been claims that the fixes for the Meltdown and Spectre security vulnerabilities, named the KPTI (aka KAISER) patches, are going to affect applicatio Read more…

By Rosemary Francis

GlobalFoundries, Ayar Labs Team Up to Commercialize Optical I/O

December 4, 2017

GlobalFoundries (GF) and Ayar Labs, a startup focused on using light, instead of electricity, to transfer data between chips, today announced they've entered in Read more…

By Tiffany Trader

Tensors Come of Age: Why the AI Revolution Will Help HPC

November 13, 2017

Thirty years ago, parallel computing was coming of age. A bitter battle began between stalwart vector computing supporters and advocates of various approaches to parallel computing. IBM skeptic Alan Karp, reacting to announcements of nCUBE’s 1024-microprocessor system and Thinking Machines’ 65,536-element array, made a public $100 wager that no one could get a parallel speedup of over 200 on real HPC workloads. Read more…

By John Gustafson & Lenore Mullin

Flipping the Flops and Reading the Top500 Tea Leaves

November 13, 2017

The 50th edition of the Top500 list, the biannual publication of the world’s fastest supercomputers based on public Linpack benchmarking results, was released Read more…

By Tiffany Trader

V100 Good but not Great on Select Deep Learning Aps, Says Xcelerit

November 27, 2017

Wringing optimum performance from hardware to accelerate deep learning applications is a challenge that often depends on the specific application in use. A benc Read more…

By John Russell

2017 Gordon Bell Prize Finalists Named

October 23, 2017

The three finalists for this year’s Gordon Bell Prize in High Performance Computing have been announced. They include two papers on projects run on China’s Read more…

By John Russell

  • arrow
  • Click Here for More Headlines
  • arrow
Share This