Start-up Aims AI at Automated Tuning of Complex Systems

By Doug Black

February 22, 2018

Today’s bigger, more complex, connected and intelligent systems have an exponentially higher number of connections, dependencies, interfaces, protocols and processing architectures that, if not optimized, will hamstring performance. For performance engineers still using manual tuning methods, systems optimization is a time-eating hydra-headed monster that poses a virtually infinite number of possible adjustments and configurations for trial-and-error testing.

Enter AI.

Start-up Concertio today launched what it said is the first machine learning-based tool aimed at making applications and systems operate for maximized performance by optimizing the myriad of configuration settings used in complex systems. While its initial product, Optimizer Studio, automates systems diagnostics and generates a “grocery list” of adjustments that systems engineers and IT managers then review and implement, the next iteration of the technology (Optimizer Runtime) will automate both diagnostics and tuning implementation.

None less than Mellanox has tested Concertio’s effectiveness on its own networking technology. As part of today’s launch, Concerto announced the results of a test involving Mellanox’s ConnectX-3 Pro Ethernet cards that compared performance after automated testing by Optimizer Studio against manual tuning methods used by Mellanox performance engineers.

The network cards were delivered in their off-the-shelf default settings, and then the Mellanox engineers and Concertio were informed of the workload the cards would be used for. Optimizer Studio ran against nine ConnectX-3 Pro specific knobs representing millions of option combinations. The tool’s workload classification engine and reinforcement learning techniques modeled the target workload, detecting different workload phases, and experimenting with various knob configurations in each phase. It then produced a report showing the optimal settings for the specific use-case.

The test result: Concertio won.

“In the comparison test, Optimizer Studio’s automated run improved performance in the target use-case by 80 percent, surpassing the 62 percent we achieved by manual tuning,” said Amir Ancel, performance group director at Mellanox. “Optimizer Studio’s automated tuning algorithms allow us to focus on high-level optimization, leaving the mundane low-level parameter optimizations to software.”

Built for traditional datacenters, hyperscale datacenters and high-performance computing systems in the cloud or on-premises, Optimizer Studio monitors and learns from the interactions between applications and systems, according to Concertio.

As of now, the tools support Linux-based systems running on Intel CPUs. Concertio said it intends to broaden its portfolio of supported technologies in upcoming product iterations.

“Tuning used to be easy,” Dr. Tomer Morad, Concertio co-founder and CEO, shared. “There were only a handful of knobs, and you’d put a performance engineer on that and tweak some settings and get some good results. But today we are already in the hundreds of knobs. It’s exploding and become almost impossible to get to a very good result because the parameter space is practically limitless. If you have 100 binary knobs it’s practically limitless, you cannot check everything. So you need some kind of automatic tool to help you with that.”

Tuning variables, called tunable knobs, include settings across hardware, firmware, the operating system and applications such as:

  • CPU hardware, including symmetric multi-threading, cache prefetching and cache partitioning configuration; peripheral hardware, including PCIe maximum read request size, network interrupt affinity and network task offloading
  • Firmware, including power states of the CPU
  • Operating system, including choice of IO or task scheduler, NUMA balancing and memory migration, thread affinity and page cache
  • Applications, including application framework settings (e.g., Spark), application component (e.g., MongoDB database) settings, and application-defined knobs

With so many variables, Morad said, performance engineers can’t be expected to know about all of the available knobs, and to predict their effects on one another. IT professionals must also occasionally tune their systems, but it’s difficult for them to maintain experts on all system internals, he said. Too often, engineers test and set optimal settings for a few knobs they are more familiar with, leaving the rest in default settings that were in place when the equipment was delivered. In some cases, he said, system tuning is overlooked entirely due to the complexity involved, and systems remain at inefficient under-performing factory settings.

Beyond application performance tuning, Morad said the tool can be used for cutting cloud and data center costs by finding system configurations that use fewer resources, and it can be utilized by hardware and software product vendors to identify optimized off-the-shelf configurations for shipment to customers or reseller. It also can be used for maximizing public benchmark performance for marketing purposes (including LINPACK).

Privately-held Concertio (previously called DatArcs) was founded in 2016 and is based in New York City. The company is part of the Runway Program at the Jacobs Technion-Cornell Institute of Cornell Tech in New York, and it’s with the Intel Ingenuity Partner Program.

“Tailor-tuned systems can significantly outperform baseline general-purpose systems, but the number of configurable settings has reached into the hundreds – way too many for any human team to effectively tune and test,” said Concertio co-founder and CTO Andrey Gelman. “It used to be merely a gap that could be bridged by human tuning and testing, but with the increasing hardware and software complexity, it’s exploding into a chasm where human performance tuning is netting diminishing returns, leaving these expensive systems bottlenecked and inefficient.”

A version of this article originally appeared on our sister site, EnterpriseTech.

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!

San Diego Supercomputer Center to Welcome ‘Expanse’ Supercomputer in 2020

July 18, 2019

With a $10 million dollar award from the National Science Foundation, San Diego Supercomputer Center (SDSC) at the University of California San Diego is procuring a new supercomputer, called Expanse, to be deployed next Read more…

By Staff report

Informing Designs of Safer, More Efficient Aircraft with Exascale Computing

July 18, 2019

During the process of designing an aircraft, aeronautical engineers must perform predictive simulations to understand how airflow around the plane impacts flight characteristics. However, modeling the complexities and su Read more…

By Rob Johnson

How Fast is Your Rubik Solver; This One’s Probably Faster

July 18, 2019

In the race to solve Rubik’s Cube, the time-to-finish keeps shrinking. This year Philipp Weyer from Germany won the 10th World Cube Association (WCA) Championship held in Melbourne, Australia, with a 6.74-second perfo Read more…

By John Russell

HPE Extreme Performance Solutions

Bring the Combined Power of HPC and AI to Your Business Transformation

A growing number of commercial businesses are implementing HPC solutions to derive actionable business insights, to run higher performance applications and to gain a competitive advantage. Read more…

IBM Accelerated Insights

Smarter Technology Revs Up Red Bull Racing

In 21st century business, companies that effectively leverage their information resources – thrive. As it turns out, the same is true in Formula One racing. Read more…

Intel Debuts Pohoiki Beach, Its 8M Neuron Neuromorphic Development System

July 17, 2019

Neuromorphic computing has received less fanfare of late than quantum computing whose mystery has captured public attention and which seems to have generated more efforts (academic, government, and commercial) but whose Read more…

By John Russell

Informing Designs of Safer, More Efficient Aircraft with Exascale Computing

July 18, 2019

During the process of designing an aircraft, aeronautical engineers must perform predictive simulations to understand how airflow around the plane impacts fligh Read more…

By Rob Johnson

Intel Debuts Pohoiki Beach, Its 8M Neuron Neuromorphic Development System

July 17, 2019

Neuromorphic computing has received less fanfare of late than quantum computing whose mystery has captured public attention and which seems to have generated mo Read more…

By John Russell

Goonhilly Unveils New Immersion-Cooled Platform, Doubles Down on Sustainability Mission

July 16, 2019

Goonhilly Earth Station has opened its new datacenter – an enhancement to its existing tier 3 facility – in Cornwall, England, touting an ambitious commitme Read more…

By Oliver Peckham

ISC19 Cluster Competition: Application Results, Finally!

July 15, 2019

Our exhaustive coverage of the ISC19 Student Cluster Competition continues as we discuss the application scores below. While the scores were typically high, som Read more…

By Dan Olds

Nvidia Expands DGX-Ready AI Program to 19 Countries

July 11, 2019

Nvidia’s DGX-Ready Data Center Program, announced in January and designed to provide colo and public cloud-like options to access the company’s GPU-powered Read more…

By Doug Black

Argonne Team Makes Record Globus File Transfer

July 10, 2019

A team of scientists at Argonne National Laboratory has broken a data transfer record by moving a staggering 2.9 petabytes of data for a research project.  The data – from three large cosmological simulations – was generated and stored on the Summit supercomputer at the Oak Ridge Leadership Computing Facility (OLCF)... Read more…

By Oliver Peckham

Nvidia, Google Tie in Second MLPerf Training ‘At-Scale’ Round

July 10, 2019

Results for the second round of the AI benchmarking suite known as MLPerf were published today with Google Cloud and Nvidia each picking up three wins in the at Read more…

By Tiffany Trader

Applied Materials Embedding New Memory Technologies in Chips

July 9, 2019

Applied Materials, the $17 billion Santa Clara-based materials engineering company for the semiconductor industry, today announced manufacturing systems enablin Read more…

By Doug Black

High Performance (Potato) Chips

May 5, 2006

In this article, we focus on how Procter & Gamble is using high performance computing to create some common, everyday supermarket products. Tom Lange, a 27-year veteran of the company, tells us how P&G models products, processes and production systems for the betterment of consumer package goods. Read more…

By Michael Feldman

Cray, AMD to Extend DOE’s Exascale Frontier

May 7, 2019

Cray and AMD are coming back to Oak Ridge National Laboratory to partner on the world’s largest and most expensive supercomputer. The Department of Energy’s Read more…

By Tiffany Trader

Graphene Surprises Again, This Time for Quantum Computing

May 8, 2019

Graphene is fascinating stuff with promise for use in a seeming endless number of applications. This month researchers from the University of Vienna and Institu Read more…

By John Russell

AMD Verifies Its Largest 7nm Chip Design in Ten Hours

June 5, 2019

AMD announced last week that its engineers had successfully executed the first physical verification of its largest 7nm chip design – in just ten hours. The AMD Radeon Instinct Vega20 – which boasts 13.2 billion transistors – was tested using a TSMC-certified Calibre nmDRC software platform from Mentor. Read more…

By Oliver Peckham

TSMC and Samsung Moving to 5nm; Whither Moore’s Law?

June 12, 2019

With reports that Taiwan Semiconductor Manufacturing Co. (TMSC) and Samsung are moving quickly to 5nm manufacturing, it’s a good time to again ponder whither goes the venerable Moore’s law. Shrinking feature size has of course been the primary hallmark of achieving Moore’s law... Read more…

By John Russell

Deep Learning Competitors Stalk Nvidia

May 14, 2019

There is no shortage of processing architectures emerging to accelerate deep learning workloads, with two more options emerging this week to challenge GPU leader Nvidia. First, Intel researchers claimed a new deep learning record for image classification on the ResNet-50 convolutional neural network. Separately, Israeli AI chip startup Hailo.ai... Read more…

By George Leopold

Nvidia Embraces Arm, Declares Intent to Accelerate All CPU Architectures

June 17, 2019

As the Top500 list was being announced at ISC in Frankfurt today with an upgraded petascale Arm supercomputer in the top third of the list, Nvidia announced its Read more…

By Tiffany Trader

Top500 Purely Petaflops; US Maintains Performance Lead

June 17, 2019

With the kick-off of the International Supercomputing Conference (ISC) in Frankfurt this morning, the 53rd Top500 list made its debut, and this one's for petafl Read more…

By Tiffany Trader

Leading Solution Providers

ISC 2019 Virtual Booth Video Tour

CRAY
CRAY
DDN
DDN
DELL EMC
DELL EMC
GOOGLE
GOOGLE
ONE STOP SYSTEMS
ONE STOP SYSTEMS
PANASAS
PANASAS
VERNE GLOBAL
VERNE GLOBAL

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

Cray – and the Cray Brand – to Be Positioned at Tip of HPE’s HPC Spear

May 22, 2019

More so than with most acquisitions of this kind, HPE’s purchase of Cray for $1.3 billion, announced last week, seems to have elements of that overused, often Read more…

By Doug Black and Tiffany Trader

A Behind-the-Scenes Look at the Hardware That Powered the Black Hole Image

June 24, 2019

Two months ago, the first-ever image of a black hole took the internet by storm. A team of scientists took years to produce and verify the striking image – an Read more…

By Oliver Peckham

Announcing four new HPC capabilities in Google Cloud Platform

April 15, 2019

When you’re running compute-bound or memory-bound applications for high performance computing or large, data-dependent machine learning training workloads on Read more…

By Wyatt Gorman, HPC Specialist, Google Cloud; Brad Calder, VP of Engineering, Google Cloud; Bart Sano, VP of Platforms, Google Cloud

Chinese Company Sugon Placed on US ‘Entity List’ After Strong Showing at International Supercomputing Conference

June 26, 2019

After more than a decade of advancing its supercomputing prowess, operating the world’s most powerful supercomputer from June 2013 to June 2018, China is keep 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

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

In Wake of Nvidia-Mellanox: Xilinx to Acquire Solarflare

April 25, 2019

With echoes of Nvidia’s recent acquisition of Mellanox, FPGA maker Xilinx has announced a definitive agreement to acquire Solarflare Communications, provider Read more…

By Doug Black

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