InfiniBand In-Network Computing Technology Accelerates Top HPC and Artificial Intelligence Infrastructures

By Gilad Shainer

May 29, 2018

The latest revolution in HPC and Artificial Intelligence is reflected in the effort around the new Data-Centric architecture. This architecture recognizes that data is the most important asset to any organization or business, and our ability to find insights, design new products and enhance science depends on the ability to analyze the growing amounts of data, as fast as possible. The old data center concept of CPU-Centric architecture has reached the limits of its scalability. Compute and storage infrastructures need to design not around the CPU but around the data, which means the ability to analyze data everywhere. Therefore the new generations of data center interconnect, will incorporate In-Network Computing technologies that share the responsibility for handling and accelerating application workloads.

Interconnects based on In-Networking computing enable offloading not only the entire range of network functions from the CPU to the network (aka network transport and RDMA), but various  data algorithms as well. Offloading data algorithms to the network allows users to run these algorithms on the data while the data is being transferred within the system interconnect, rather than waiting for the data to reach a CPU. In-Network Computing transforms the data center interconnect into a “distributed CPU,” and “distributed memory,” to overcome performance bottlenecks and enable faster and more scalable data analysis. One of the leading technologies under the In-Networking Computing architecture is Scalable Hierarchal Aggregation and Reduction Protocol (SHARP)™.

Collective communication is a term used to describe communication patterns amongst all members of a communication endpoint group. For example, in the case of Message Passing Interface (MPI), the communication end-points are MPI processes and the groups associated with the collective operation are described by the local and remote groups associated with the MPI communicator. Generally, one may define many types of collective operations. The MPI standard defines blocking and non-blocking forms of barrier synchronization, broadcast, gather, scatter, gather to all, all-to-all gather/scatter, reduction, reduce-scatter, and scan. The OpenSHMEM specification defines blocking barrier synchronization, broadcast, collect, and reduction forms of collective operations.

The performance of collective operations for applications that use such functions is often critical to the overall performance of these applications, as it defines performance and scalability. Additionally, the explicit coupling between communication end-points tends to magnify the effects of system noise on the parallel applications by delaying one or more data exchanges, resulting in further application scalability challenges. Enhancing operational performance can no longer by achieved by merely adding more CPUs. In fact, adding more CPUs to the system can actually hurt the collective’s performance and increase operational latency.

On account of the large impact collective operations has on overall application performance and scalability, Mellanox has invested considerable effort in optimizing the performance of such operations. This includes enhancing the Host Channel Adapter (HCA) with CORE-Direct™ application offloading technology, which was developed jointly by Mellanox and Oak Ridge National Laboratory and received the R&D100 award.

SHARP further improves the performance of collective operations by processing the data as it traverses the network, eliminating the need to send data multiple times between end-points. The first stage of SHARP introduced with the EDR InfiniBand generation, supports performance- critical barrier and small data reduction collective operations. The second generation of SHARP to be introduced with the HDR InfiniBand generation extends support for large data collectives as well.

Figure 1 and 2 demonstrate the performance advantages of SHARP, using the MPI AllReduce collective operation. The testing was implemented on the new InfiniBand-accelerated Dragonfly+ Niagara supercomputer, the fastest supercomputer in Canada. Niagara, which is owned by the University of Toronto and operated by SciNet, is designed to enable large parallel jobs. Niagara was designed to optimize throughput of a range of scientific codes running at scale, energy efficiency, and network and storage performance and capacity. Niagara consists of 1500 nodes, each node has 40 Intel Skylake cores at 2.4GHz, for a total of 60,000 cores, and 202 GB of RAM per node, all connected with EDR InfiniBand network in a Dragonfly+ topology.

Figure 1 – MPI AllReduce performance comparison – Software-based versus SHARP with 1 process per node, and overall 1,500 MPI ranks
Figure 1 – MPI AllReduce performance comparison – Software-based versus SHARP with 1 process per node, and overall 1,500 MPI ranks

 

 

Figure 2 - MPI AllReduce performance comparison – Software-based versus SHARP with 40 processes per node, and overall 60,000 MPI ranks
Figure 2 – MPI AllReduce performance comparison – Software-based versus SHARP with 40 processes per node, and overall 60,000 MPI ranks

 

Both graphs demonstrated the performance advantages of SHARP – including a dramatic reduction in AllReduce latency – up to 8X higher performance, combined with a reduction in data motion and of course, in CPU utilization, which means freeing up CPU cycles needed for other tasks.

Figure 3 – InfiniBand-based, top supercomputers around the world (examples)
Figure 3 – InfiniBand-based, top supercomputers around the world (examples)

Scalable Hierarchal Aggregation and Reduction Protocol (SHARP) technology is one of the main In-Network Computing architecture elements. Other technologies include the ability to offload MPI Tag-Matching and the MPI Rendezvous protocol from the CPU (software) to the network. In-Network Computing is the cutting-edge advantage of InfiniBand interconnect. It feeds intelligence into the network that connects the top supercomputers around the word, accelerating high-performance computing and artificial intelligence applications.

 

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!

Data West Brings Technology Leaders to SDSC

December 6, 2018

Data and technology enthusiasts from around the world descended upon the San Diego Supercomputing Center (SDSC) for the third annual Data West conference, which is taking place this week on the campus of the University o Read more…

By Alex Woodie

Topology Can Help Us Find Patterns in Weather

December 6, 2018

Topology--–the study of shapes-- seems to be all the rage. You could even say that data has shape, and shape matters. Shapes are comfortable and familiar concepts, so it is intriguing to see that many applications are Read more…

By James Reinders

What’s New in HPC Research: Automatic Energy Efficiency, DNA Data Analysis, Post-Exascale & More

December 6, 2018

In this bimonthly feature, HPCwire highlights newly published research in the high-performance computing community and related domains. From exascale to quantum computing, the details are here. Read more…

By Oliver Peckham

HPE Extreme Performance Solutions

AI Can Be Scary. But Choosing the Wrong Partners Can Be Mortifying!

As you continue to dive deeper into AI, you will discover it is more than just deep learning. AI is an extremely complex set of machine learning, deep learning, reinforcement, and analytics algorithms with varying compute, storage, memory, and communications needs. Read more…

IBM Accelerated Insights

Five Steps to Building a Data Strategy for AI

Our data-centric world is driving many organizations to apply advanced analytics that use artificial intelligence (AI). AI provides intelligent answers to challenging business questions. AI also enables highly personalized user experiences, built when data scientists and analysts learn new information from data that would otherwise go undetected using traditional analytics methods. Read more…

Zettascale by 2035? China Thinks So

December 6, 2018

Exascale machines (of at least a 1 exaflops peak) are anticipated to arrive by around 2020, a few years behind original predictions; and given extreme-scale performance challenges are not getting any easier, it makes sense that researchers are already looking ahead to the next big 1,000x performance goal post: zettascale computing. Read more…

By Tiffany Trader

Topology Can Help Us Find Patterns in Weather

December 6, 2018

Topology--–the study of shapes-- seems to be all the rage. You could even say that data has shape, and shape matters. Shapes are comfortable and familiar conc Read more…

By James Reinders

Zettascale by 2035? China Thinks So

December 6, 2018

Exascale machines (of at least a 1 exaflops peak) are anticipated to arrive by around 2020, a few years behind original predictions; and given extreme-scale performance challenges are not getting any easier, it makes sense that researchers are already looking ahead to the next big 1,000x performance goal post: zettascale computing. Read more…

By Tiffany Trader

Robust Quantum Computers Still a Decade Away, Says Nat’l Academies Report

December 5, 2018

The National Academies of Science, Engineering, and Medicine yesterday released a report – Quantum Computing: Progress and Prospects – whose optimism about Read more…

By John Russell

Revisiting the 2008 Exascale Computing Study at SC18

November 29, 2018

A report published a decade ago conveyed the results of a study aimed at determining if it were possible to achieve 1000X the computational power of the the Read more…

By Scott Gibson

AWS Debuts Lustre as a Service, Accelerates Data Transfer

November 28, 2018

From the Amazon re:Invent main stage in Las Vegas today, Amazon Web Services CEO Andy Jassy introduced Amazon FSx for Lustre, citing a growing body of applicati Read more…

By Tiffany Trader

AWS Launches First Arm Cloud Instances

November 28, 2018

AWS, a macrocosm of the emerging high-performance technology landscape, wants to be everywhere you want to be and offer everything you want to use (or at least Read more…

By Doug Black

Move Over Lustre & Spectrum Scale – Here Comes BeeGFS?

November 26, 2018

Is BeeGFS – the parallel file system with European roots – on a path to compete with Lustre and Spectrum Scale worldwide in HPC environments? Frank Herold Read more…

By John Russell

DOE Under Secretary for Science Paul Dabbar Interviewed at SC18

November 21, 2018

During the 30th annual SC conference in Dallas last week, SC18 hosted U.S. Department of Energy Under Secretary for Science Paul M. Dabbar. In attendance Nov. 13-14, Dabbar delivered remarks at the Top500 panel, met with a number of industry stakeholders and toured the show floor. He also met with HPCwire for an interview, where we discussed the role of the DOE in advancing leadership computing. Read more…

By Tiffany Trader

Quantum Computing Will Never Work

November 27, 2018

Amid the gush of money and enthusiastic predictions being thrown at quantum computing comes a proposed cold shower in the form of an essay by physicist Mikhail Read more…

By John Russell

Cray Unveils Shasta, Lands NERSC-9 Contract

October 30, 2018

Cray revealed today the details of its next-gen supercomputing architecture, Shasta, selected to be the next flagship system at NERSC. We've known of the code-name "Shasta" since the Argonne slice of the CORAL project was announced in 2015 and although the details of that plan have changed considerably, Cray didn't slow down its timeline for Shasta. Read more…

By Tiffany Trader

IBM at Hot Chips: What’s Next for Power

August 23, 2018

With processor, memory and networking technologies all racing to fill in for an ailing Moore’s law, the era of the heterogeneous datacenter is well underway, Read more…

By Tiffany Trader

House Passes $1.275B National Quantum Initiative

September 17, 2018

Last Thursday the U.S. House of Representatives passed the National Quantum Initiative Act (NQIA) intended to accelerate quantum computing research and developm Read more…

By John Russell

CERN Project Sees Orders-of-Magnitude Speedup with AI Approach

August 14, 2018

An award-winning effort at CERN has demonstrated potential to significantly change how the physics based modeling and simulation communities view machine learni Read more…

By Rob Farber

Summit Supercomputer is Already Making its Mark on Science

September 20, 2018

Summit, now the fastest supercomputer in the world, is quickly making its mark in science – five of the six finalists just announced for the prestigious 2018 Read more…

By John Russell

AMD Sets Up for Epyc Epoch

November 16, 2018

It’s been a good two weeks, AMD’s Gary Silcott and Andy Parma told me on the last day of SC18 in Dallas at the restaurant where we met to discuss their show news and recent successes. Heck, it’s been a good year. Read more…

By Tiffany Trader

US Leads Supercomputing with #1, #2 Systems & Petascale Arm

November 12, 2018

The 31st Supercomputing Conference (SC) - commemorating 30 years since the first Supercomputing in 1988 - kicked off in Dallas yesterday, taking over the Kay Ba Read more…

By Tiffany Trader

Leading Solution Providers

SC 18 Virtual Booth Video Tour

Advania @ SC18 AMD @ SC18
ASRock Rack @ SC18
DDN Storage @ SC18
HPE @ SC18
IBM @ SC18
Lenovo @ SC18 Mellanox Technologies @ SC18
NVIDIA @ SC18
One Stop Systems @ SC18
Oracle @ SC18 Panasas @ SC18
Supermicro @ SC18 SUSE @ SC18 TYAN @ SC18
Verne Global @ SC18

TACC’s ‘Frontera’ Supercomputer Expands Horizon for Extreme-Scale Science

August 29, 2018

The National Science Foundation and the Texas Advanced Computing Center announced today that a new system, called Frontera, will overtake Stampede 2 as the fast Read more…

By Tiffany Trader

HPE No. 1, IBM Surges, in ‘Bucking Bronco’ High Performance Server Market

September 27, 2018

Riding healthy U.S. and global economies, strong demand for AI-capable hardware and other tailwind trends, the high performance computing server market jumped 28 percent in the second quarter 2018 to $3.7 billion, up from $2.9 billion for the same period last year, according to industry analyst firm Hyperion Research. Read more…

By Doug Black

Nvidia’s Jensen Huang Delivers Vision for the New HPC

November 14, 2018

For nearly two hours on Monday at SC18, Jensen Huang, CEO of Nvidia, presented his expansive view of the future of HPC (and computing in general) as only he can do. Animated. Backstopped by a stream of data charts, product photos, and even a beautiful image of supernovae... Read more…

By John Russell

Germany Celebrates Launch of Two Fastest Supercomputers

September 26, 2018

The new high-performance computer SuperMUC-NG at the Leibniz Supercomputing Center (LRZ) in Garching is the fastest computer in Germany and one of the fastest i Read more…

By Tiffany Trader

Houston to Field Massive, ‘Geophysically Configured’ Cloud Supercomputer

October 11, 2018

Based on some news stories out today, one might get the impression that the next system to crack number one on the Top500 would be an industrial oil and gas mon Read more…

By Tiffany Trader

Intel Confirms 48-Core Cascade Lake-AP for 2019

November 4, 2018

As part of the run-up to SC18, taking place in Dallas next week (Nov. 11-16), Intel is doling out info on its next-gen Cascade Lake family of Xeon processors, specifically the “Advanced Processor” version (Cascade Lake-AP), architected for high-performance computing, artificial intelligence and infrastructure-as-a-service workloads. Read more…

By Tiffany Trader

Google Releases Machine Learning “What-If” Analysis Tool

September 12, 2018

Training machine learning models has long been time-consuming process. Yesterday, Google released a “What-If Tool” for probing how data point changes affect a model’s prediction. The new tool is being launched as a new feature of the open source TensorBoard web application... Read more…

By John Russell

The Convergence of Big Data and Extreme-Scale HPC

August 31, 2018

As we are heading towards extreme-scale HPC coupled with data intensive analytics like machine learning, the necessary integration of big data and HPC is a curr Read more…

By Rob Farber

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