Work Smarter Not Harder: Platform Analytics Simplifies HPC Infrastructure Analysis

By Nicole Hemsoth

January 4, 2012

By: Gord Sissons, Product Marketing Manager, Platform Computing

High Performance Computing (HPC) data centers are crucibles of innovation and have pioneered advancements such as distributed cluster computing, parallel programming techniques and smart workload scheduling. While modern HPC data centers run at higher levels of efficiency than their commercial counterparts, there is always a need to process higher volumes of complex data in less time, resulting in additional challenges for data center managers. These challenges include dealing with rapid hardware and software advancements; tight or sometimes shrinking budgets; and the need to balance the demands of competing project teams with shifting priorities.

To boost efficiency, most HPC data centers turn to workload managers, which enable resources to be shared among users and project teams according to policy. However, while workload managers are good at enforcing policies; they can’t determine what those policies should be – and after delivering this “low-hanging fruit” in efficiency gains, further improvements become progressively more difficult.

The key to efficiency lies in providing senior managers and decision makers with better information, which in turn help them make better decisions. To analyze the effectiveness of an HPC environment, it is important to collect information about infrastructure (host models, capacities, networks, OS types); how infrastructure is used (application types, resource usage patterns); clusters and queue configurations (composition, scheduling policies); job-related statistics (run-times, pending times, failure rates, resource usage); projects, users and groups; and license inventories and usage patterns.

Turning Data into Knowledge

Workload managers simplify reporting by gathering and aggregating data into database tables, challenges remain. They include:

  • Reporting systems may not incorporate all sources of data, making some questions impossible to answer;
  • Reports and underlying data structures are fixed so users can only ask questions that the database schema is designed to readily answer; and
  • Workload managers can also be costly to develop and maintain, and answering a new question may require significant development time.

When evaluating analysis and visualization tools for their HPC data centers, organizations should evaluate solutions using the following criteria:

Resource Optimization to Control Costs: By understanding exactly how resources are used, by whom and for what purpose, scheduling policies can be adjusted to provide better utilization and overall efficiency. By turning raw data into usable information, trends and changes in usage patterns become obvious quickly. By visualizing how the need for different applications and platforms are changing with time by project or department, planners can make better quality data-driven decisions more quickly. They can consolidate under-used assets and ensure that new spending is aligned optimally to the needs of the business.

Full Visibility into HPC Data Center Operations: Analysts are able to constantly test and validate planning assumptions and make mid-course corrections as needed. With proper analysis tools they can ensure that SLAs are being met and that business critical projects have ample resources. By analyzing key measures like pending time and license denials across different data dimensions, managers and analysts can be confident that users have access to critical resources when needed, but at minimum cost.

Ability to Identify Bottlenecks: By analyzing resource use and service levels together, administrators quickly spot delays impacting productivity. By understanding underlying causes rather than just symptoms, capacity and performance problems can be solved rapidly, often without incremental cost.

Usage reporting and chargeback accounting: Some organizations like to apportion costs between client departments based on measured resource usage. By combining resource, license and job level data, administrators can track and view resource usage by user, department or project. The rich capabilities of analytics software can make it possible to implement sophisticated chargeback accounting solutions tailored to the needs of the organization.

The Business Intelligence Advantage

A good approach for analyzing HPC infrastructure is the use of on-line analytics processing technology (OLAP) widely used in business intelligence applications. OLAP cubes store measures over multiple data dimensions enabling information to be analyzed and manipulated quickly from multiple perspectives.

The superiority of this analytical approach has led HPC vendors to offer OLAP-based infrastructure analysis solutions, including Platform Analytics from Platform Computing. The main challenge with OLAP is the sheer amount of data that needs to be collected, processed and analyzed. Depending on factors like data volumes and retention policies, data volumes can grow massively. Data sets of several terabytes are common.

Analyzing Efficiency with Rational OLAP Technology

While OLAP represents the best approach for analyzing the effectiveness of HPC environments, its use is usually limited to larger data centers due to the associated cost and complexity. Fortunately, recent innovations including Relational OLAP technology (ROLAP) and fast column-oriented databases now provide the means to address these limitations, making advanced analytics practical for smaller HPC environments as well.

ROLAP technology is an alternative to traditional multi-dimensional OLAP that avoids the pre-computation and storage of information in intermediate formats. Rather, it accomplishes the same functionally with standard SQL queries instead. This allows data center managers and analysts to perform full multi-dimensional analysis while avoiding the cost and complexity of pre-building cubes. With ROLAP-based solutions, users have access to their data immediately without waiting for intermediate data marts and cubes to be built involving multi-step time and resource intensive ETL process.

Parallel, Column-Oriented Databases

Another enabler is new types of grid-oriented databases that use column-based organizational strategies for storing data. Since this approach involves reading columns rather than rows, reads can be parallelized and distributed across multiple compute hosts on a cluster, which is made possible by the columns being independent of one another. With appropriate data replication to ensure integrity, columnar databases can be implemented using a “shared nothing” model and distributed on commodity compute hosts. Scaling the database performance becomes a matter of simply adding hosts.

A higher degree of data compression is also possible because data columns are of a homogeneous type and are stored together. Better compression reduces both data storage requirements and data transfer times. However, once a database server runs out of capacity, they become difficult and costly to enlarge. Database architects are often required to employ clustering technology or expensive SAN solutions to increase capacity.

To illustrate the performance gains, Platform Computing tested a traditional relational database compared to a column-oriented database and found loading 11 million records was measured to be 13 times faster using a column-oriented database. Even more significant, query performance in data sets ranging from 15 million to 1 billion records was measured to be between 78 and 100 times faster – a two orders-of-magnitude improvement.

Platform Analytics

By exploiting these advances and supplying a powerful new user interface, Platform Computing has developed an analysis and reporting platform that is simpler, more powerful and less costly to deploy and maintain than competing analysis solutions. This means that even smaller HPC environments can now benefit from the insights that advanced analysis tools can deliver.

Platform Analytics 8 is a next-generation analysis and visualization tool for Platform LSF. It enables analysts and managers to answer business-level questions quickly and easily while aggregating job, resource and license-usage data from multiple clusters boosting productivity and enabling data-driven decision-making.

Unlike analytics solutions that require extensive data manipulation to represent data in a usable form, Platform Analytics 8 incorporates a state-of-the-art ROLAP visualization tool. It also features several pre-built “dashboards” designed to cater information to various audiences, including users, project managers, IT personnel, administrators and line of business executives. With Platform Analytics, cluster administrators can “drill” into detailed data to examine the effectiveness of resource sharing policies, while executives can focus on key-performance indicators and relevant cost, productivity and utilization metrics.

Summary

As HPC environments increase in complexity they become progressively more difficult for analysts, managers and business planners to fully understand. Small inefficiencies tend to accumulate and multiply over time driving costs, slowing problem identification and resolution, and diminishing productivity.

By applying modern analytic methods pioneered in business intelligence, HPC managers and analysts can gain important new insights into their environments. Platform Analytics 8 leverages these recent advancements to provide rich analysis capabilities for Platform LSF. With better tools, managers and planners have access to higher quality information faster. With better information, they can “work smarter” by realizing gains in efficiency and productivity while simultaneously containing costs.

This article was based on the “Work Smarter Not Harder: Easier Said Than Done?” whitepaper. The full whitepaper is available for download here (registration is required).

###

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!

Weekly Twitter Roundup (Feb. 23, 2017)

February 23, 2017

Here at HPCwire, we aim to keep the HPC community apprised of the most relevant and interesting news items that get tweeted throughout the week. Read more…

By Thomas Ayres

HPE Server Shows Low Latency on STAC-N1 Test

February 22, 2017

The performance of trade and match servers can be a critical differentiator for financial trading houses. Read more…

By John Russell

HPC Financial Update (Feb. 2017)

February 22, 2017

In this recurring feature, we’ll provide you with financial highlights from companies in the HPC industry. Check back in regularly for an updated list with the most pertinent fiscal information. Read more…

By Thomas Ayres

Rethinking HPC Platforms for ‘Second Gen’ Applications

February 22, 2017

Just what constitutes HPC and how best to support it is a keen topic currently. Read more…

By John Russell

HPE Extreme Performance Solutions

O&G Companies Create Value with High Performance Remote Visualization

Today’s oil and gas (O&G) companies are striving to process datasets that have become not only tremendously large, but extremely complex. And the larger that data becomes, the harder it is to move and analyze it – particularly with a workforce that could be distributed between drilling sites, offshore rigs, and remote offices. Read more…

HPC Technique Propels Deep Learning at Scale

February 21, 2017

Researchers from Baidu’s Silicon Valley AI Lab (SVAIL) have adapted a well-known HPC communication technique to boost the speed and scale of their neural network training and now they are sharing their implementation with the larger deep learning community. Read more…

By Tiffany Trader

IDC: Will the Real Exascale Race Please Stand Up?

February 21, 2017

So the exascale race is on. And lots of organizations are in the pack. Government announcements from the US, China, India, Japan, and the EU indicate that they are working hard to make it happen – some sooner, some later. Read more…

By Bob Sorensen, IDC

ExxonMobil, NCSA, Cray Scale Reservoir Simulation to 700,000+ Processors

February 17, 2017

In a scaling breakthrough for oil and gas discovery, ExxonMobil geoscientists report they have harnessed the power of 717,000 processors – the equivalent of 22,000 32-processor computers – to run complex oil and gas reservoir simulation models. Read more…

By Doug Black

TSUBAME3.0 Points to Future HPE Pascal-NVLink-OPA Server

February 17, 2017

Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a new board design for NVLink-equipped Pascal P100 GPUs that will create another entrant to the space currently occupied by Nvidia's DGX-1 system, IBM's "Minsky" platform and the Supermicro SuperServer (1028GQ-TXR). Read more…

By Tiffany Trader

HPC Technique Propels Deep Learning at Scale

February 21, 2017

Researchers from Baidu’s Silicon Valley AI Lab (SVAIL) have adapted a well-known HPC communication technique to boost the speed and scale of their neural network training and now they are sharing their implementation with the larger deep learning community. Read more…

By Tiffany Trader

IDC: Will the Real Exascale Race Please Stand Up?

February 21, 2017

So the exascale race is on. And lots of organizations are in the pack. Government announcements from the US, China, India, Japan, and the EU indicate that they are working hard to make it happen – some sooner, some later. Read more…

By Bob Sorensen, IDC

TSUBAME3.0 Points to Future HPE Pascal-NVLink-OPA Server

February 17, 2017

Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a new board design for NVLink-equipped Pascal P100 GPUs that will create another entrant to the space currently occupied by Nvidia's DGX-1 system, IBM's "Minsky" platform and the Supermicro SuperServer (1028GQ-TXR). Read more…

By Tiffany Trader

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which will be Japan’s “fastest AI supercomputer,” Read more…

By Tiffany Trader

Drug Developers Use Google Cloud HPC in the Fight Against ALS

February 16, 2017

Within the haystack of a lethal disease such as ALS (amyotrophic lateral sclerosis / Lou Gehrig’s Disease) there exists, somewhere, the needle that will pierce this therapy-resistant affliction. Read more…

By Doug Black

Azure Edges AWS in Linpack Benchmark Study

February 15, 2017

The “when will clouds be ready for HPC” question has ebbed and flowed for years. Read more…

By John Russell

Is Liquid Cooling Ready to Go Mainstream?

February 13, 2017

Lost in the frenzy of SC16 was a substantial rise in the number of vendors showing server oriented liquid cooling technologies. Three decades ago liquid cooling was pretty much the exclusive realm of the Cray-2 and IBM mainframe class products. That’s changing. We are now seeing an emergence of x86 class server products with exotic plumbing technology ranging from Direct-to-Chip to servers and storage completely immersed in a dielectric fluid. Read more…

By Steve Campbell

Cray Posts Best-Ever Quarter, Visibility Still Limited

February 10, 2017

On its Wednesday earnings call, Cray announced the largest revenue quarter in the company’s history and the second-highest revenue year. Read more…

By Tiffany Trader

For IBM/OpenPOWER: Success in 2017 = (Volume) Sales

January 11, 2017

To a large degree IBM and the OpenPOWER Foundation have done what they said they would – assembling a substantial and growing ecosystem and bringing Power-based products to market, all in about three years. Read more…

By John Russell

US, China Vie for Supercomputing Supremacy

November 14, 2016

The 48th edition of the TOP500 list is fresh off the presses and while there is no new number one system, as previously teased by China, there are a number of notable entrants from the US and around the world and significant trends to report on. Read more…

By Tiffany Trader

Lighting up Aurora: Behind the Scenes at the Creation of the DOE’s Upcoming 200 Petaflops Supercomputer

December 1, 2016

In April 2015, U.S. Department of Energy Undersecretary Franklin Orr announced that Intel would be the prime contractor for Aurora: Read more…

By Jan Rowell

D-Wave SC16 Update: What’s Bo Ewald Saying These Days

November 18, 2016

Tucked in a back section of the SC16 exhibit hall, quantum computing pioneer D-Wave has been talking up its new 2000-qubit processor announced in September. Forget for a moment the criticism sometimes aimed at D-Wave. This small Canadian company has sold several machines including, for example, ones to Lockheed and NASA, and has worked with Google on mapping machine learning problems to quantum computing. In July Los Alamos National Laboratory took possession of a 1000-quibit D-Wave 2X system that LANL ordered a year ago around the time of SC15. Read more…

By John Russell

Enlisting Deep Learning in the War on Cancer

December 7, 2016

Sometime in Q2 2017 the first ‘results’ of the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) will become publicly available according to Rick Stevens. He leads one of three JDACS4C pilot projects pressing deep learning (DL) into service in the War on Cancer. Read more…

By John Russell

IBM Wants to be “Red Hat” of Deep Learning

January 26, 2017

IBM today announced the addition of TensorFlow and Chainer deep learning frameworks to its PowerAI suite of deep learning tools, which already includes popular offerings such as Caffe, Theano, and Torch. Read more…

By John Russell

HPC Startup Advances Auto-Parallelization’s Promise

January 23, 2017

The shift from single core to multicore hardware has made finding parallelism in codes more important than ever, but that hasn’t made the task of parallel programming any easier. Read more…

By Tiffany Trader

CPU Benchmarking: Haswell Versus POWER8

June 2, 2015

With OpenPOWER activity ramping up and IBM’s prominent role in the upcoming DOE machines Summit and Sierra, it’s a good time to look at how the IBM POWER CPU stacks up against the x86 Xeon Haswell CPU from Intel. Read more…

By Tiffany Trader

Leading Solution Providers

Nvidia Sees Bright Future for AI Supercomputing

November 23, 2016

Graphics chipmaker Nvidia made a strong showing at SC16 in Salt Lake City last week. Read more…

By Tiffany Trader

BioTeam’s Berman Charts 2017 HPC Trends in Life Sciences

January 4, 2017

Twenty years ago high performance computing was nearly absent from life sciences. Today it’s used throughout life sciences and biomedical research. Genomics and the data deluge from modern lab instruments are the main drivers, but so is the longer-term desire to perform predictive simulation in support of Precision Medicine (PM). There’s even a specialized life sciences supercomputer, ‘Anton’ from D.E. Shaw Research, and the Pittsburgh Supercomputing Center is standing up its second Anton 2 and actively soliciting project proposals. There’s a lot going on. Read more…

By John Russell

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which will be Japan’s “fastest AI supercomputer,” Read more…

By Tiffany Trader

IDG to Be Bought by Chinese Investors; IDC to Spin Out HPC Group

January 19, 2017

US-based publishing and investment firm International Data Group, Inc. (IDG) will be acquired by a pair of Chinese investors, China Oceanwide Holdings Group Co., Ltd. Read more…

By Tiffany Trader

Dell Knights Landing Machine Sets New STAC Records

November 2, 2016

The Securities Technology Analysis Center, commonly known as STAC, has released a new report characterizing the performance of the Knight Landing-based Dell PowerEdge C6320p server on the STAC-A2 benchmarking suite, widely used by the financial services industry to test and evaluate computing platforms. The Dell machine has set new records for both the baseline Greeks benchmark and the large Greeks benchmark. Read more…

By Tiffany Trader

What Knights Landing Is Not

June 18, 2016

As we get ready to launch the newest member of the Intel Xeon Phi family, code named Knights Landing, it is natural that there be some questions and potentially some confusion. Read more…

By James Reinders, Intel

Is Liquid Cooling Ready to Go Mainstream?

February 13, 2017

Lost in the frenzy of SC16 was a substantial rise in the number of vendors showing server oriented liquid cooling technologies. Three decades ago liquid cooling was pretty much the exclusive realm of the Cray-2 and IBM mainframe class products. That’s changing. We are now seeing an emergence of x86 class server products with exotic plumbing technology ranging from Direct-to-Chip to servers and storage completely immersed in a dielectric fluid. Read more…

By Steve Campbell

KNUPATH Hermosa-based Commercial Boards Expected in Q1 2017

December 15, 2016

Last June tech start-up KnuEdge emerged from stealth mode to begin spreading the word about its new processor and fabric technology that’s been roughly a decade in the making. Read more…

By John Russell

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