Accelerate Hadoop MapReduce Performance using Dedicated OrangeFS Servers

By Nicole Hemsoth

September 9, 2013

Recent tests performed at Clemson University achieved a 25 percent improvement in Apache Hadoop Terasort run times by replacing Hadoop Distributed File System (HDFS) with an OrangeFS configuration using dedicated servers. Key components included extension of the MapReduce “FileSystem” class and a Java Native Interface (JNI) shim to the OrangeFS client. No modifications of Hadoop were required, and existing MapReduce jobs require no modification to utilize OrangeFS. The results also demonstrated the ability to deploy MapReduce with a general purpose High Performance file system in a High Performance Computing (HPC) environment, increasing potential for more flexible workflow.

The open source Hadoop MapReduce project has a traditional hardware architecture that differs from standard HPC architecture, where thin clients access remote, shared, and potentially distributed data servers. With HDFS, clients and data servers are paired together, running on the same hardware. Many HPC sites would like to extend their cluster use to support Hadoop MapReduce. With OrangeFS providing distributed storage as part of HPC clusters, they could leverage their existing investment in HPC to run Hadoop MapReduce workloads.

Through testing this configuration, a number of benefits emerged:

  • MapReduce clients accessing a dedicated OrangeFS storage cluster yielded a 25 percent faster combined run time than the traditional approach, where MapReduce clients access data locally for the three operations (teragen, terasort, and teravalidate).
  • OrangeFS and HDFS, without replication enabled, performed similarly under identical local (traditional HDFS) configurations (within 0.2 percent); however, OrangeFS adds the advantages of a general purpose, scale-out file system. With a general purpose file system, applications can read and write data to OrangeFS while it remains available for Hadoop MapReduce job input, improving run time by eliminating time-consuming HDFS stage-in and stage-out operations.
  • Doubling the number of compute nodes accessing the OrangeFS cluster results in ~300 percent improvement on Terasort job run time.
  • OrangeFS provides good results when clients significantly overcommit storage servers.

About OrangeFS

OrangeFS is a user-friendly, open-source, next-generation parallel file system for compute and storage clusters of the future. OrangeFS increases IO performance by storing a file in objects across multiple servers and accessing these objects in parallel. Offering more feature rich data access and manipulation than HDFS, OrangeFS is an ideal tool for storing, processing and analyzing data with MapReduce. A staff of developers support OrangeFS, improving stability and functionality for the base system and developing new interfaces.

OrangeFS has an object-based infrastructure. Each file and directory consists of two or more objects: one primarily containing file metadata, and the other(s) primarily containing file data. Objects may contain both data and metadata as needed to fulfill their role in the file system. This division and distribution of data to the servers is imperceptible to users, who see a traditional, logical file view. The OrangeFS distributed file structure provides outstanding scalability in performance and capacity.

OrangeFS client interfaces work with a range of operating systems, including Linux, Mac OS X and Windows. Compatible client interfaces include Direct Interface, WebDAV, S3, REST, FUSE, Hadoop and MPI-IO.

OrangeFS with Hadoop MapReduce

Hadoop’s abstract FileSystem class allows MapReduce to leverage file systems other than HDFS, with a configuration file that sets the designated file system. Hadoop MapReduce is written in Java, but OrangeFS’s client libraries are written in C. A Java Native Interface (JNI) shim allows data to be passed between programs, avoiding the overhead of memory copies with Java’s NIO Direct ByteBuffer. The JNI shim allows Java code to execute functions present in the OrangeFS Direct Client Interface. The OrangeFS Direct Client Interface Library is a collection of familiar POSIX-like and system standard input/output (stdio.h) library calls designed for parallel access to OrangeFS. OrangeFS differs from HDFS in that it allows modification of data after the initial write.

The Terasort benchmarks successfully explored potential for replacing HDFS with the prerelease version 2.8.8 of OrangeFS, working with the Hadoop 1.x stable release. The two Hadoop configurations which were evaluated are shown in Figure 1.

File System Test Configuration 

Figure 1 Test Configurations

Test Protocols

Hadoop MapReduce File System Test (Figure 2)

To test the impact of replacing HDFS with OrangeFS, developers performed a full terabyte (1 TB) Terasort benchmark on 8 nodes, each running both MapReduce and the file system shown in the first configuration above. The tests were performed on 8 Dell PowerEdge R720s with local SSDs for metadata and 12 2-TB drives for data. In this test, MapReduce ran locally on the same nodes, first over OrangeFS and then over HDFS, interconnected with 10Gb/s Ethernet. Both file systems used the compute nodes for storage as well.

Hadoop MapReduce Remote Client Test

Using the same benchmarks with typical HPC storage architecture, another test, “OFS Remote” in Figure 2, measured how MapReduce performs when data is stored on dedicated, network-connected storage nodes running OrangeFS. Eight additional nodes were used as MapReduce clients, and eight Dell PowerEdge R720s with local SSDs for metadata and 12 2-TB drives for data were used as storage nodes only, shown in the Remote Client Test Configuration in Figure 1.

Results

OrangeFS decreased Terasort run time in the dedicated OrangeFS storage cluster architecture by about 25 percent over the traditional MapReduce architecture, where clients access data from local disks. OrangeFS and HDFS, without replication enabled, performed similarly under identical local (traditional HDFS) configurations (within 0.2 percent); however, OrangeFS adds the advantages of a general purpose, scale-out file system.

Figure 2 Hadoop MapReduce File System Test 

Figure 2 Hadoop MapReduce File System Test

Hadoop MapReduce over OrangeFS with Overcommitted Storage Servers (Figure 3)

A separate test evaluated MapReduce over OrangeFS, overcommitting the storage nodes and evaluating how well this approach scales with more MapReduce clients than storage nodes. The Terasort test was performed with an increasing number of clients utilizing a dedicated OrangeFS cluster composed of 16 Dell PowerEdge R720s with local SSDs for metadata and 12 2-TB drives for data. The Hadoop client nodes had only a single hard disk drive available for intermediate data storage purposes, increasing the time over previous tests where Hadoop clients possessed 12 disks. If the clients used a solid state drive (SSD) for storage and retrieval of intermediate data instead, the slowdown caused by the single disk compared to an array of disks would be alleviated to some extent.

Figure 3 Hadoop MapReduce over OrangeFS with Overcommitted Storage Servers 

Figure 3 Hadoop MapReduce over OrangeFS with Overcommitted Storage Servers

Results

In testing 16, 32, and 64 compute nodes, doubling the number of compute nodes caused a ~300 percent improvement on Terasort job run time. OrangeFS provides good results when clients significantly overcommit the storage servers (4 to 1 in these tests). While providing improvements as a good general purpose file system for MapReduce, OrangeFS is also an excellent concurrent working file system to support the storage needs of other applications while simultaneously serving Hadoop MapReduce.

Benefits

  • OrangeFS enables modification of data anywhere in a file, while HDFS requires copying data before modification, except in the case of Append in the Hadoop 2.x release.
  • OrangeFS replaces the HDFS single namenode with multiple OrangeFS metadata/data servers, reducing task time with improved scalability and eliminating this single point of contention.
  • Potentially, intermediate data can also be written to OrangeFS rather than a temporary folder on each Hadoop client disk, optionally retaining it for use in future jobs and further improving performance with OrangeFS serving the data to MapReduce.

Obstacles

  • Unlike HDFS, OrangeFS doesn’t currently support built-in replication. (OrangeFS can be run in High Availability (HA) mode, and plans for the 3.0 release of OrangeFS include integrated replication for both data and metadata.)
  • OrangeFS and Hadoop are separate installations which must be configured to work together. (Plans for the 2.8.8 release of OrangeFS include a more comprehensive documentation set, including instructions for using Hadoop’s MapReduce with the OrangeFS file system.)

Conclusion

The results demonstrated that replacing HDFS with OrangeFS produced better MapReduce performance for workloads with high volumes of intermediate data, i.e., terasort.

Separating MapReduce clients from storage servers can provide stability in the case of client failure, without the overhead of replication, and eases local disk contention during the reduce stage.

Hadoop MapReduce can leverage OrangeFS as its underlying storage system in an HPC environment. A Portable Batch System (PBS) or Sun Grid Engine (SGE) scheduled HPC environment can support on-demand Hadoop MapReduce clusters deployed and configured automatically, using the open source project “myHadoop.” Researchers could customize a version of myHadoop to schedule on-demand MapReduce clusters, with data persisting on OrangeFS, eliminating HDFS’s time consuming data stage-in and stage-out phases. (myHadoop scripts will be available with the next release of OrangeFS, for running jobs in a scheduled environment.)

Future evaluations may test how performance could be improved, since Hadoop Map and Reduce tasks could be patched to support reading and writing intermediate data to OrangeFS, rather than local disk, improving job run time with faster I/O rates.

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!

US Exascale Computing Update with Paul Messina

December 8, 2016

Around the world, efforts are ramping up to cross the next major computing threshold with machines that are 50-100x more performant than today’s fastest number crunchers.  Read more…

By Tiffany Trader

Weekly Twitter Roundup (Dec. 8, 2016)

December 8, 2016

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

Qualcomm Targets Intel Datacenter Dominance with 10nm ARM-based Server Chip

December 8, 2016

Claiming no less than a reshaping of the future of Intel-dominated datacenter computing, Qualcomm Technologies, the market leader in smartphone chips, announced the forthcoming availability of what it says is the world’s first 10nm processor for servers, based on ARM Holding’s chip designs. Read more…

By Doug Black

Which Schools Produce the Top Coders in the World?

December 8, 2016

Ever wonder which universities worldwide produce the best coders? The answers may surprise you, at least as judged by the results of a competition posted yesterday on the HackerRank blog. 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. The pilots, supported in part by DOE exascale funding, not only seek to do good by advancing cancer research and therapy but also to advance deep learning capabilities and infrastructure with an eye towards eventual use on exascale machines. Read more…

By John Russell

DDN Enables 50TB/Day Trans-Pacific Data Transfer for Yahoo Japan

December 6, 2016

Transferring data from one data center to another in search of lower regional energy costs isn’t a new concept, but Yahoo Japan is putting the idea into transcontinental effect with a system that transfers 50TB of data a day from Japan to the U.S., where electricity costs a quarter of the rates in Japan. Read more…

By Doug Black

Infographic Highlights Career of Admiral Grace Murray Hopper

December 5, 2016

Dr. Grace Murray Hopper (December 9, 1906 – January 1, 1992) was an early pioneer of computer science and one of the most famous women achievers in a field dominated by men. Read more…

By Staff

Ganthier, Turkel on the Dell EMC Road Ahead

December 5, 2016

Who is Dell EMC and why should you care? Glad you asked is Jim Ganthier’s quick response. Ganthier is SVP for validated solutions and high performance computing for the new (even bigger) technology giant Dell EMC following Dell’s acquisition of EMC in September. In this case, says Ganthier, the blending of the two companies is a 1+1 = 5 proposition. Not bad math if you can pull it off. Read more…

By John Russell

US Exascale Computing Update with Paul Messina

December 8, 2016

Around the world, efforts are ramping up to cross the next major computing threshold with machines that are 50-100x more performant than today’s fastest number crunchers.  Read more…

By Tiffany Trader

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. The pilots, supported in part by DOE exascale funding, not only seek to do good by advancing cancer research and therapy but also to advance deep learning capabilities and infrastructure with an eye towards eventual use on exascale machines. Read more…

By John Russell

Ganthier, Turkel on the Dell EMC Road Ahead

December 5, 2016

Who is Dell EMC and why should you care? Glad you asked is Jim Ganthier’s quick response. Ganthier is SVP for validated solutions and high performance computing for the new (even bigger) technology giant Dell EMC following Dell’s acquisition of EMC in September. In this case, says Ganthier, the blending of the two companies is a 1+1 = 5 proposition. Not bad math if you can pull it off. Read more…

By John Russell

AWS Launches Massive 100 Petabyte ‘Sneakernet’

December 1, 2016

Amazon Web Services now offers a way to move data into its cloud by the truckload. 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

Seagate-led SAGE Project Delivers Update on Exascale Goals

November 29, 2016

Roughly a year and a half after its launch, the SAGE exascale storage project led by Seagate has delivered a substantive interim report – Data Storage for Extreme Scale. Read more…

By John Russell

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

HPE-SGI to Tackle Exascale and Enterprise Targets

November 22, 2016

At first blush, and maybe second blush too, Hewlett Packard Enterprise’s (HPE) purchase of SGI seems like an unambiguous win-win. SGI’s advanced shared memory technology, its popular UV product line (Hanna), deep vertical market expertise, and services-led go-to-market capability all give HPE a leg up in its drive to remake itself. Bear in mind HPE came into existence just a year ago with the split of Hewlett-Packard. The computer landscape, including HPC, is shifting with still unclear consequences. One wonders who’s next on the deal block following Dell’s recent merger with EMC. Read more…

By John Russell

Why 2016 Is the Most Important Year in HPC in Over Two Decades

August 23, 2016

In 1994, two NASA employees connected 16 commodity workstations together using a standard Ethernet LAN and installed open-source message passing software that allowed their number-crunching scientific application to run on the whole “cluster” of machines as if it were a single entity. Read more…

By Vincent Natoli, Stone Ridge Technology

IBM Advances Against x86 with Power9

August 30, 2016

After offering OpenPower Summit attendees a limited preview in April, IBM is unveiling further details of its next-gen CPU, Power9, which the tech mainstay is counting on to regain market share ceded to rival Intel. Read more…

By Tiffany Trader

AWS Beats Azure to K80 General Availability

September 30, 2016

Amazon Web Services has seeded its cloud with Nvidia Tesla K80 GPUs to meet the growing demand for accelerated computing across an increasingly-diverse range of workloads. The P2 instance family is a welcome addition for compute- and data-focused users who were growing frustrated with the performance limitations of Amazon's G2 instances, which are backed by three-year-old Nvidia GRID K520 graphics cards. Read more…

By Tiffany Trader

Think Fast – Is Neuromorphic Computing Set to Leap Forward?

August 15, 2016

Steadily advancing neuromorphic computing technology has created high expectations for this fundamentally different approach to computing. Read more…

By John Russell

The Exascale Computing Project Awards $39.8M to 22 Projects

September 7, 2016

The Department of Energy’s Exascale Computing Project (ECP) hit an important milestone today with the announcement of its first round of funding, moving the nation closer to its goal of reaching capable exascale computing by 2023. Read more…

By Tiffany Trader

ARM Unveils Scalable Vector Extension for HPC at Hot Chips

August 22, 2016

ARM and Fujitsu today announced a scalable vector extension (SVE) to the ARMv8-A architecture intended to enhance ARM capabilities in HPC workloads. Fujitsu is the lead silicon partner in the effort (so far) and will use ARM with SVE technology in its post K computer, Japan’s next flagship supercomputer planned for the 2020 timeframe. This is an important incremental step for ARM, which seeks to push more aggressively into mainstream and HPC server markets. Read more…

By John Russell

IBM Debuts Power8 Chip with NVLink and Three New Systems

September 8, 2016

Not long after revealing more details about its next-gen Power9 chip due in 2017, IBM today rolled out three new Power8-based Linux servers and a new version of its Power8 chip featuring Nvidia’s NVLink interconnect. Read more…

By John Russell

Vectors: How the Old Became New Again in Supercomputing

September 26, 2016

Vector instructions, once a powerful performance innovation of supercomputing in the 1970s and 1980s became an obsolete technology in the 1990s. But like the mythical phoenix bird, vector instructions have arisen from the ashes. Here is the history of a technology that went from new to old then back to new. Read more…

By Lynd Stringer

Leading Solution Providers

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

HPE Gobbles SGI for Larger Slice of $11B HPC Pie

August 11, 2016

Hewlett Packard Enterprise (HPE) announced today that it will acquire rival HPC server maker SGI for $7.75 per share, or about $275 million, inclusive of cash and debt. The deal ends the seven-year reprieve that kept the SGI banner flying after Rackable Systems purchased the bankrupt Silicon Graphics Inc. for $25 million in 2009 and assumed the SGI brand. Bringing SGI into its fold bolsters HPE's high-performance computing and data analytics capabilities and expands its position... Read more…

By Tiffany Trader

Intel Launches Silicon Photonics Chip, Previews Next-Gen Phi for AI

August 18, 2016

At the Intel Developer Forum, held in San Francisco this week, Intel Senior Vice President and General Manager Diane Bryant announced the launch of Intel's Silicon Photonics product line and teased a brand-new Phi product, codenamed "Knights Mill," aimed at machine learning workloads. 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

Beyond von Neumann, Neuromorphic Computing Steadily Advances

March 21, 2016

Neuromorphic computing – brain inspired computing – has long been a tantalizing goal. The human brain does with around 20 watts what supercomputers do with megawatts. And power consumption isn’t the only difference. Fundamentally, brains ‘think differently’ than the von Neumann architecture-based computers. While neuromorphic computing progress has been intriguing, it has still not proven very practical. Read more…

By John Russell

Dell EMC Engineers Strategy to Democratize HPC

September 29, 2016

The freshly minted Dell EMC division of Dell Technologies is on a mission to take HPC mainstream with a strategy that hinges on engineered solutions, beginning with a focus on three industry verticals: manufacturing, research and life sciences. "Unlike traditional HPC where everybody bought parts, assembled parts and ran the workloads and did iterative engineering, we want folks to focus on time to innovation and let us worry about the infrastructure," said Jim Ganthier, senior vice president, validated solutions organization at Dell EMC Converged Platforms Solution Division. Read more…

By Tiffany Trader

Container App ‘Singularity’ Eases Scientific Computing

October 20, 2016

HPC container platform Singularity is just six months out from its 1.0 release but already is making inroads across the HPC research landscape. It's in use at Lawrence Berkeley National Laboratory (LBNL), where Singularity founder Gregory Kurtzer has worked in the High Performance Computing Services (HPCS) group for 16 years. Read more…

By Tiffany Trader

Micron, Intel Prepare to Launch 3D XPoint Memory

August 16, 2016

Micron Technology used last week’s Flash Memory Summit to roll out its new line of 3D XPoint memory technology jointly developed with Intel while demonstrating the technology in solid-state drives. Micron claimed its Quantx line delivers PCI Express (PCIe) SSD performance with read latencies at less than 10 microseconds and writes at less than 20 microseconds. Read more…

By George Leopold

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