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!

‘Business Value’ of AI Heads Toward $4 Trillion

April 26, 2018

The rise of AI is reflected in recent market forecasts that predict it will help enterprises develop new products and services around applications like automated decision making. Market analyst Gartner Inc. forecasts Read more…

By George Leopold

Former AMD Chip Chief and ‘Zen’ Architect Jim Keller Joins Intel

April 26, 2018

Intel announced today it has hired top microprocessor architect Jim Keller as senior vice president to lead the company’s silicon engineering group, focusing on system-on-chip (SoC) development and integration. Read more…

By Tiffany Trader

Rackspace Is Latest to Roll Bare Metal Service

April 26, 2018

Rackspace is expanding its managed private cloud services with the addition of six new bare metal instances that it collectively refers to as bare metal as a service. The private cloud vendor announced the new managed Read more…

By George Leopold

HPE Extreme Performance Solutions

Hybrid HPC is Speeding Time to Insight and Revolutionizing Medicine

High performance computing (HPC) is a key driver of success in many verticals today, and health and life science industries are extensively leveraging these capabilities. Read more…

Google Charts Two-Dimensional Quantum Course

April 26, 2018

Quantum error correction, essential for achieving universal fault-tolerant quantum computation, is one of the main challenges of the quantum computing field and it’s top of mind for Google’s John Martinis. At a presentation last week at the HPC User Forum in Tucson, Martinis, one of the world's foremost experts in quantum computing, emphasized... Read more…

By Tiffany Trader

Google Charts Two-Dimensional Quantum Course

April 26, 2018

Quantum error correction, essential for achieving universal fault-tolerant quantum computation, is one of the main challenges of the quantum computing field and it’s top of mind for Google’s John Martinis. At a presentation last week at the HPC User Forum in Tucson, Martinis, one of the world's foremost experts in quantum computing, emphasized... Read more…

By Tiffany Trader

Affordable Optical Technology Needed Says HPE’s Daley

April 26, 2018

While not new, the challenges presented by computer cabling/PCB circuit routing design – cost, performance, space requirements, and power management – have Read more…

By John Russell

AI-Focused ‘Genius’ Supercomputer Installed at KU Leuven

April 24, 2018

Hewlett Packard Enterprise has deployed a new approximately half-petaflops supercomputer, named Genius, at Flemish research university KU Leuven. The system is Read more…

By Tiffany Trader

Cray Rolls Out AMD-Based CS500; More to Follow?

April 18, 2018

Cray was the latest OEM to bring AMD back into the fold with introduction today of a CS500 option based on AMD’s Epyc processor line. The move follows Cray’ Read more…

By John Russell

IBM: Software Ecosystem for OpenPOWER is Ready for Prime Time

April 16, 2018

With key pieces of the IBM/OpenPOWER versus Intel/x86 gambit settling into place – e.g., the arrival of Power9 chips and Power9-based systems, hyperscaler sup Read more…

By John Russell

US Plans $1.8 Billion Spend on DOE Exascale Supercomputing

April 11, 2018

On Monday, the United States Department of Energy announced its intention to procure up to three exascale supercomputers at a cost of up to $1.8 billion with th Read more…

By Tiffany Trader

Cloud-Readiness and Looking Beyond Application Scaling

April 11, 2018

There are two aspects to consider when determining if an application is suitable for running in the cloud. The first, which we will discuss here under the title Read more…

By Chris Downing

Transitioning from Big Data to Discovery: Data Management as a Keystone Analytics Strategy

April 9, 2018

The past 10-15 years has seen a stark rise in the density, size, and diversity of scientific data being generated in every scientific discipline in the world. Key among the sciences has been the explosion of laboratory technologies that generate large amounts of data in life-sciences and healthcare research. Large amounts of data are now being stored in very large storage name spaces, with little to no organization and a general unease about how to approach analyzing it. Read more…

By Ari Berman, BioTeam, Inc.

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

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

How the Cloud Is Falling Short for HPC

March 15, 2018

The last couple of years have seen cloud computing gradually build some legitimacy within the HPC world, but still the HPC industry lies far behind enterprise I Read more…

By Chris Downing

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

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

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

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

Deep Learning at 15 PFlops Enables Training for Extreme Weather Identification at Scale

March 19, 2018

Petaflop per second deep learning training performance on the NERSC (National Energy Research Scientific Computing Center) Cori supercomputer has given climate Read more…

By Rob Farber

Leading Solution Providers

Lenovo Unveils Warm Water Cooled ThinkSystem SD650 in Rampup to LRZ Install

February 22, 2018

This week Lenovo took the wraps off the ThinkSystem SD650 high-density server with third-generation direct water cooling technology developed in tandem with par 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

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

HPC and AI – Two Communities Same Future

January 25, 2018

According to Al Gara (Intel Fellow, Data Center Group), high performance computing and artificial intelligence will increasingly intertwine as we transition to Read more…

By Rob Farber

US Plans $1.8 Billion Spend on DOE Exascale Supercomputing

April 11, 2018

On Monday, the United States Department of Energy announced its intention to procure up to three exascale supercomputers at a cost of up to $1.8 billion with th Read more…

By Tiffany Trader

New Blueprint for Converging HPC, Big Data

January 18, 2018

After five annual workshops on Big Data and Extreme-Scale Computing (BDEC), a group of international HPC heavyweights including Jack Dongarra (University of Te Read more…

By John Russell

Momentum Builds for US Exascale

January 9, 2018

2018 looks to be a great year for the U.S. exascale program. The last several months of 2017 revealed a number of important developments that help put the U.S. Read more…

By Alex R. Larzelere

Google Chases Quantum Supremacy with 72-Qubit Processor

March 7, 2018

Google pulled ahead of the pack this week in the race toward "quantum supremacy," with the introduction of a new 72-qubit quantum processor called Bristlecone. Read more…

By Tiffany Trader

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