Does Your Cluster Scale?

By Benoit Marchand

September 22, 2006

Introduction

This article will discuss both the hidden and painfully obvious scaling inefficiencies inherent in current technology commodity cluster and Grid computing with respect to data movement. It will also discuss the latest advances in parallel file serving technology and how these techniques can be utilized with current network topologies, file servers and unmodified applications to deliver throughput performance speed-ups of order 2X to 9X on certain workloads.

Commodity Clusters: The Promise and the Reality

IDC reported that in 2005 clusters represented about 50 percent of high performance and technical computing systems sales. Cluster sales have been growing very rapidly, driven by the desire to lower TCO for computationally intensive workloads.

I think there is a world market for maybe five computers.
    – Thomas Watson, Chairman of IBM, 1943

Perhaps you have more than five computers in your cluster. Using commodity components to build high-throughput clusters makes tremendous sense for delivering affordable processing power. In theory if your application is highly parallel, the run-time should reduce almost linearly as additional processing nodes are allocated to the application workload. However, the move away from SMPs to clusters has caused the file serving capability to become an external resource.

If you were plowing a field, which would you rather use? Two strong oxen or 1024 chickens?
    – Seymour Cray, father of supercomputing

On “throughput-oriented data intensive” workloads, cluster administrators and users alike are discovering that performance not only fails to scale as more nodes are added, but the throughput actually starts to degrade. This phenomenon is perhaps best understood [at a high level] if you consider Seymour Cray's definition of a supercomputer: “a device that turns a compute-bound problem into an I/O-bound problem.”

Clearly this is the case for commodity cluster technology today. As commodity processor technology accelerates with Moore's Law, the network bandwidth and file serving capabilities are falling farther and farther behind. How can we address the I/O corollary to Amdahl's law (the slowest component governs overall performance) in a way that does not add significantly to the cost structure of a cluster? Parallel file serving technology offers an answer.

Typically on clusters today, Workload Managers (WLMs) assign jobs to nodes based on application license availability and the cluster or grid's computational node characteristics (CPU availability, memory, local disk, etc.) Consequently, jobs are assigned to cluster nodes and each cluster node then attempts to acquire the requisite data file(s) from the file system (be it NFS or a parallel file system) through the network in a haphazard manner. This results in an I/O traffic jam that throttles the efficiency and scaling of throughput applications on the cluster.

This phenomenon of poor data provisioning results in the I/O time eventually swamping the compute time as node count increases. Poor data provisioning starves the cluster, resulting in a throughput performance curve that resembles a quadratic in the performance vs. node count space — that is, run time improves as the number of nodes increases, but relatively less with each added node, up to some inflection point, then the time per job increases absolutely due to the continually increasing I/O burden becoming overwhelming.

Often these inefficiencies go unnoticed when one has not yet reached the inflection point, since the cluster CPU utilization, the network utilization and the file system all seem to be performing at acceptable levels. However, further analysis reveals that the cluster CPUs are busy waiting for data to arrive across the network from the file system and are NOT busy doing productive work.

Are you getting what you paid for?

When cluster administrators are asked whether their cluster is utilized efficiently, most would instantly and resoundingly say “YES.” Often it's the users who have experienced performance degradation (or less improvement than expected) after a hardware upgrade who look at this issue more closely.

There are a number of methods available to measure the total efficiency of your cluster on specific applications. The easiest method is explained in the following two steps:

1. First, edit your run scripts and add “/bin/time” (or an equivalent command) where applications are launched. Then run the application on your entire cluster at once, using your normal method of file serving (e.g. NFS from a NAS system or parallel file system).

/bin/time blastall –p ...

2. Second, try pre-staging your files. Run the same script on a single node (or all nodes if you wish) but first transfer all input files to local disk (on each cluster node) and designate output files to be written locally. Collect the timings and calculate the efficiency (“user + system / real”). Compare the NFS (or other file serving mechanism) to the local disk resident results. You will likely be surprised to discover that you have a data serving bottleneck.

real: 160.1
user:  80.3
sys:    1.6

efficiency: 51 percent

Typically such tests take just a few minutes to set up and to analyze results. So with a minimum time investment on your part you can get an accurate picture of your cluster's processing efficiency on your favorite applications.

How parallel file serving technology can improve cluster efficiency

With a clear understanding of what limits cluster efficiency, let's look at ways to boost file serving capacity. Distributed file systems, parallel file systems, high bandwidth file serving devices, etc. exhibit better performance characteristics than standard NFS topologies, but do not provide a quantum leap in performance and are often prohibitively expensive to acquire. In the case of some parallel file systems, they are additionally very expensive when it comes time to expand storage capacity.

Parallel file serving technology with sophisticated error recovery and fault resilience techniques offer a method of replicating data sets ahead of applications being pushed to nodes. This is a novel and tantalizing approach to solving the data provisioning problem plaguing commodity clusters and edge grids today. However, replication as a parallel file serving technology is not a general-purpose solution and the characteristics of an application need to be looked at to determine whether this approach will significantly accelerate cluster throughput.

The ideal application profile is where the same, or substantially the same, large (300 MB to 50 GB) data set is being sent to each node of a cluster that utilizes 32 or more nodes. Examples of these types of ideal codes include Genome Searches (NCBI-BLAST), Medical Imaging, Weather Analysis, Rendering, etc.

However, many parallel applications work by splitting a problem via domain decomposition, such that each sub-task requires just a portion of the entire data set. So why replicate the whole data set on each node? Well there are many reasons.

First, there is always some overlap in the data set requirements between sub-tasks (boundary zones) requiring that part of the data set is sent many times over on the network. In such cases replicating the overlapped sections is more efficient.

Second, ordinarily each node runs multiple sub-tasks and over time each node will have utilized a significant proportion of the entire data set. In practice data sets are sent repeatedly over the network consuming bandwidth and aggravating network congestion. It makes sense then to send the data set just once to all nodes concurrently.

Third, when sub-tasks start to execute I/O, capacity is limited by the lesser of file serving and network capacity. In general file serving is less than 200 MB/s on a large cluster (500 MB/s with high-end specialized file servers). But when data is replicated to nodes the I/O capacity is linearly proportional to the number of nodes. So at 50 MB/s per node a cluster of 100 nodes can generate 5 GB/sec of I/O capacity,  over an order of magnitude better than “top of the line” file servers.

Fourth, one has to take into account the file serving overhead which replication does not experience. The following is a real-world example of such overhead:

Recently CeBiTec, a leading bioinformatics research center located at Bielefeld University in Germany, tested the following scenario for parallel file serving performance. They provisioned 935 MB to each of 120 nodes over 1 Gbit Ethernet using a 250 MB/s file server with CacheFS and then, with replication technology. As you can see by the results below, replication from data residing on the file server was just as fast as CacheFS with a 100 percent hit rate on the local nodes and no dirty cache entries (the absolute best case for CacheFS). Compared to CacheFS with a dirty cache, or to regular V3 NFS, replication was over 20 times faster. This means that in the ideal case for file serving operation, where no actual data movement occurs (all data is perfectly cached on all nodes), it is still no better than straight replication from the original file server. This is what file serving overheads can do to degrade your cluster efficiency.

Method                            Time (mm:ss)

NFS v3                            14:50
NFS CacheFS (cache filled)         0:30  (estimated)
NFS CacheFS (cache dirty)         11:50
Replication                        0:35

Finally, few people believe replication can be made to scale on large clusters. The same research center also tested the scalability of replication. At 10 nodes replication to every node took 45 seconds and at 120 nodes it took just 35 seconds; better than linear scaling. While that result is somewhat unusual, nearly linear performance is typically seen, the point being that replicating to 100s of nodes costs about the same time as replicating to a few nodes.

What has to change to use replication?

Migration to a replication process takes very little time. There is no need to change the applications, Workload Manager, file server, networking or OS environment. Simply edit your job scripts and point to the local cache where appropriate when starting applications. Users can be up and running within the first day.

It is possible to address the scaling issue faced with data-intensive throughput workloads by employing parallel file serving technology, provided the technology supports sophisticated error recovery and fault resilience techniques and is designed for scalability to large numbers of nodes/clients. The technology should:

  • Solve synchronization, error recovery and back-up recovery issues of replication
  • Automatically pre-stage data, which means that while nodes are running a set of processes the data needed for the next set is pre-loaded in the background, thus completely eliminating file serving latencies and network bottlenecks
  • Automatically synchronize with Workload Managers to ensure that jobs only run when data is ready to be used at the nodes
  • Establish an efficient, asynchronous processing pipeline for both input and output data
  • Automatically clean up data sets that are no longer used
  • Automatically resynchronize nodes after a crash, automating complex and tedious management chores

With scalable parallel file serving, high performance technical computing on clusters becomes a reality for many more users.

Make everything as simple as possible, but not simpler.
    – Albert Einstein

—–

Benoit Marchand is the CEO and founder of eXludus and has been active in the field of high performance computing and distributed processing applications for over 20 years. He has held management positions at Sun Microsystems and SGI. He received an MBA from HEC in Montreal and a master's degree in computer science from the University of Waterloo.

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!

Doug Kothe on the Race to Build Exascale Applications

May 29, 2017

Ensuring there are applications ready to churn out useful science when the first U.S. exascale computers arrive in the 2021-2023 timeframe is Doug Kothe’s job Read more…

By John Russell

PRACEdays Reflects Europe’s HPC Commitment

May 25, 2017

More than 250 attendees and participants came together for PRACEdays17 in Barcelona last week, part of the European HPC Summit Week 2017, held May 15-19 at t Read more…

By Tiffany Trader

Russian Researchers Claim First Quantum-Safe Blockchain

May 25, 2017

The Russian Quantum Center today announced it has overcome the threat of quantum cryptography by creating the first quantum-safe blockchain, securing cryptocurr Read more…

By Doug Black

Google Debuts TPU v2 and will Add to Google Cloud

May 25, 2017

Not long after stirring attention in the deep learning/AI community by revealing the details of its Tensor Processing Unit (TPU), Google last week announced the Read more…

By John Russell

HPE Extreme Performance Solutions

Exploring the Three Models of Remote Visualization

The explosion of data and advancement of digital technologies are dramatically changing the way many companies do business. With the help of high performance computing (HPC) solutions and data analytics platforms, manufacturers are developing products faster, healthcare providers are improving patient care, and energy companies are improving planning, exploration, and production. Read more…

Nvidia CEO Predicts AI ‘Cambrian Explosion’

May 25, 2017

The processing power and cloud access to developer tools used to train machine-learning models are making artificial intelligence ubiquitous across computing pl Read more…

By George Leopold

PGAS Use will Rise on New H/W Trends, Says Reinders

May 25, 2017

If you have not already tried using PGAS, it is time to consider adding PGAS to the programming techniques you know. Partitioned Global Array Space, commonly kn Read more…

By James Reinders

Exascale Escapes 2018 Budget Axe; Rest of Science Suffers

May 23, 2017

President Trump's proposed $4.1 trillion FY 2018 budget is good for U.S. exascale computing development, but grim for the rest of science and technology spend Read more…

By Tiffany Trader

Hedge Funds (with Supercomputing help) Rank First Among Investors

May 22, 2017

In case you didn’t know, The Quants Run Wall Street Now, or so says a headline in today’s Wall Street Journal. Quant-run hedge funds now control the largest Read more…

By John Russell

Doug Kothe on the Race to Build Exascale Applications

May 29, 2017

Ensuring there are applications ready to churn out useful science when the first U.S. exascale computers arrive in the 2021-2023 timeframe is Doug Kothe’s job Read more…

By John Russell

PRACEdays Reflects Europe’s HPC Commitment

May 25, 2017

More than 250 attendees and participants came together for PRACEdays17 in Barcelona last week, part of the European HPC Summit Week 2017, held May 15-19 at t Read more…

By Tiffany Trader

PGAS Use will Rise on New H/W Trends, Says Reinders

May 25, 2017

If you have not already tried using PGAS, it is time to consider adding PGAS to the programming techniques you know. Partitioned Global Array Space, commonly kn Read more…

By James Reinders

Exascale Escapes 2018 Budget Axe; Rest of Science Suffers

May 23, 2017

President Trump's proposed $4.1 trillion FY 2018 budget is good for U.S. exascale computing development, but grim for the rest of science and technology spend Read more…

By Tiffany Trader

Cray Offers Supercomputing as a Service, Targets Biotechs First

May 16, 2017

Leading supercomputer vendor Cray and datacenter/cloud provider the Markley Group today announced plans to jointly deliver supercomputing as a service. The init Read more…

By John Russell

HPE’s Memory-centric The Machine Coming into View, Opens ARMs to 3rd-party Developers

May 16, 2017

Announced three years ago, HPE’s The Machine is said to be the largest R&D program in the venerable company’s history, one that could be progressing tow Read more…

By Doug Black

What’s Up with Hyperion as It Transitions From IDC?

May 15, 2017

If you’re wondering what’s happening with Hyperion Research – formerly the IDC HPC group – apparently you are not alone, says Steve Conway, now senior V Read more…

By John Russell

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Just how close real-wo Read more…

By John Russell

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the cam Read more…

By John Russell

Google Pulls Back the Covers on Its First Machine Learning Chip

April 6, 2017

This week Google released a report detailing the design and performance characteristics of the Tensor Processing Unit (TPU), its custom ASIC for the inference Read more…

By Tiffany Trader

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its a Read more…

By Tiffany Trader

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

Since our first formal product releases of OSPRay and OpenSWR libraries in 2016, CPU-based Software Defined Visualization (SDVis) has achieved wide-spread adopt Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Last week, Google reported that its custom ASIC Tensor Processing Unit (TPU) was 15-30x faster for inferencing workloads than Nvidia's K80 GPU (see our coverage Read more…

By Tiffany Trader

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

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 ne Read more…

By Tiffany Trader

Leading Solution Providers

Facebook Open Sources Caffe2; Nvidia, Intel Rush to Optimize

April 18, 2017

From its F8 developer conference in San Jose, Calif., today, Facebook announced Caffe2, a new open-source, cross-platform framework for deep learning. Caffe2 is 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 w Read more…

By Tiffany Trader

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 Read more…

By Steve Campbell

MIT Mathematician Spins Up 220,000-Core Google Compute Cluster

April 21, 2017

On Thursday, Google announced that MIT math professor and computational number theorist Andrew V. Sutherland had set a record for the largest Google Compute Eng Read more…

By Tiffany Trader

US Supercomputing Leaders Tackle the China Question

March 15, 2017

As China continues to prove its supercomputing mettle via the Top500 list and the forward march of its ambitious plans to stand up an exascale machine by 2020, 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 networ Read more…

By Tiffany Trader

DOE Supercomputer Achieves Record 45-Qubit Quantum Simulation

April 13, 2017

In order to simulate larger and larger quantum systems and usher in an age of "quantum supremacy," researchers are stretching the limits of today's most advance Read more…

By Tiffany Trader

Knights Landing Processor with Omni-Path Makes Cloud Debut

April 18, 2017

HPC cloud specialist Rescale is partnering with Intel and HPC resource provider R Systems to offer first-ever cloud access to Xeon Phi "Knights Landing" process Read more…

By Tiffany Trader

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