Insights from Optimized Codes on Cineca’s Marconi

By Ken Strandberg

February 15, 2019

What can you do with 381,392 CPU cores? For Cineca, it means enabling computational scientists to expand a large part of the world’s body of knowledge from the nanoscale to the astronomic, from calculating quantum effects in new materials to supporting bioinformatics for advanced healthcare research to screening millions of possible chemical combinations to attack a deadly virus.

Cineca hosts Italy’s most powerful supercomputing resource at its Supercomputing Applications and Innovation (SCAI) center. Called Marconi, the 20 petaflops system provides computing for projects in Italy and across Europe. Marconi is one of the European Center of Excellence (CoE) for HPC applications, and it is part of the EuroHPC project, a joint collaboration across the EU to develop Exascale computing facilities. Among the many groups Cineca supports with supercomputing, is the Materials Design at Exascale (MaX) project, also a European CoE.

Marconi—On the Path to Exascale

“Marconi was Cineca’s first step in its roadmap to achieve exascale computing by 2022,” explained Doctor Carlo Cavazzoni, R&D director for HPC Optimization Strategies for SCAI. Cavazzoni holds a Ph.D. in Materials Science, and he is a developer for parallelized codes used in research.

“At Cineca, we always try to stay on the competitive edge with advanced computing resources,” added Cavazzoni. “We are often the first to install new technologies, such as the BlueGene/Q cluster in 2012 and Marconi’s early release of Intel Xeon Phi with Intel Omni-Path Architecture fabric in 2016 and Intel Xeon Scalable Platform in 2017.”

Having the technology first, gives developers like Cavazzoni immediate opportunities to optimize codes, such as Quantum ESPRESSO (QE), for these next-generation architectures. Cavazzoni has contributed to the QE project for 20 years. He is integral to optimizing multiple workloads that use QE on Marconi’s Intel Xeon Phi processor-based cluster. He has also supported work on proprietary plasma physics codes to run on Intel Xeon Phi’s many integrated core (MIC) architecture. Those optimizations have enabled significant speedups on projects running on Marconi.

Optimizing Codes for Marconi

“With QE we optimized the code across several directions,” said Cavazzoni. “The easiest was vectorization, because, with QE, most of the compute-intensive portion of the code is done in libraries, like the Intel Math Kernel Library—Intel MKL. With codes that perform a lot of floating-point computation, we rely on Intel MKL to exploit vectorization. Then we tuned the scalability using a hierarchy of MPI and OpenMP threading to minimize synchronization.”

According to Professor Cavazzoni, they’ve had to optimize scalability before. With a balanced design of compute, memory, and network on one supercomputer, they tuned their codes to optimize throughput. But when next-generation technologies come along, such as with MIC architecture, Intel Advanced Vector Extensions 512 (Intel AVX-512), multi-channel DRAM (MCDRAM), and 100 Gbps network, the balance changes. “The core is fast, the Intel AVX-512 makes floating point much faster, and the network is faster, but now there are sections of the code that are latency-bound and slow down synchronization,” explained Cavazzoni. “To exploit the advancements of Marconi, we had to revisit the synchronization. So, we grouped together bands in a simulation to perform a batch computation in order to adjust for this latency issue in certain parts of the code.”

But not all projects use QE. In a plasma simulation code Cavazzoni helped optimize, the problems were different. To begin with, there were no libraries. There were just big equations and lots of data.

“The plasma simulation code used Vlasov-Maxwell system of equations. It solves an explicit evolution of a six-dimensional field with three space variables and three velocity variables. So, you have a 6D model representing the plasma field, and then there’s the Maxwell equation to solve for the electrical fields,” said Cavazzoni.

To optimize this code, he first took advantage of much wider floating point registers of the processors and thus expanded the vectorization for Intel AVX-512. That delivered speedup from just the processor.

“We were also dealing with parallelization across multiple dimensions, which we were processing both locally in the node and distributed across the cluster. So, we had to find the optimum distribution of MPI and OpenMP threads in order to best fit to the Intel Xeon Phi topology, and then we needed to find the best distribution of data for the threading.”

These optimizations gave the plasma code a significant speedup due to the vectorization on one side, plus optimal distribution of threads and data on the other, according to Cavazzoni.

Discoveries at the Nanoscale

Cavazzoni provides optimization services for members of Cineca in Italy and the wider European research community working on advanced projects, such as the Materials Design at Exascale (MaX) project. Among his colleagues is Doctor Andrea Ferretti of the National Research Council (CNR), a national laboratory in Italy. Ferretti works in the field of condensed matter and solid-state physics. Like Cavazzoni, he is both a physicist and code developer. He performs ab initio simulations at the level of Density Functional Theory (DFT) and beyond using codes like QE and YAMBO.

YAMBO is a code for many-body calculations in solid state and molecular physics, which can be applied to materials and molecules.

“My involvement with developing YAMBO, along with other colleagues, is more or less the connection between myself, CRN, Cineca, and the MaX CoE,” stated Ferretti. “In MaX, we are interested in implementing high performance algorithms suitable for highly scalable large HPC machines. These include algorithms that allow us to compute properties of matter that were not possible to compute before, such as time-resolved spectroscopies or accurate thermal transport coefficients. We have also been expanding the parallelism of the code, which is crucial for running on Marconi’s Intel Xeon Phi cluster.”

One of Ferretti’s main achievements with YAMBO was to run an optimized proof of concept on a 1,000 node partition of the Intel Xeon Phi cluster.

“In that single MPI run, we were able to achieve up to 3 petaflops of performance with this code. Similarly to Quantum Monte Carlo, many-body perturbation theory methods require large-scale intense computation, so it’s ideal to use large-scale partition machines for these. We were able to maximize the performance on this cluster with our code.”

Another project Ferretti contributed to was accurate modeling of spin interfaces of molecules. Research in spin interfaces is important to “spintronics.” Spintronics is an area of technology research that uses and modifies the magnetic moment (or spin) of an electron. For example, by changing the spin on an electron, data can be encoded on a single atom. Quantum computing and extreme density magnetic storage are potential applications of electron spin. But spin interfaces using certain materials have exhibited instability over changing temperatures.

“We wanted to simulate a model of ferromagnetic and antiferromagnetic coupling of spin interfaces that had been started experimentally,” commented Ferretti. “Colleagues in the lab were able to characterize the system very well, so we knew a lot about it already and could use the data from experimentation to build a very accurate (though expensive) model to simulate.”

The system turned out to be very, very large, and they were dealing with atomic values that are very, very small.

“Magnetism at the molecular level is very dependent on a delicate balance between different total energy solutions,” added Ferretti. “The actual atomic structure of the system determines the actual magnetic coupling. Yet, we are looking at energy differences that are six or seven orders of magnitude smaller than the total energies we can compute. So, if we want our model to be predictive, we need to be as precise as possible; we cannot oversimplify the problem with loose approximations. That takes a lot of computing power.”

Marconi made it possible to simulate the system as close as possible to the experimental data. The researchers were able to go on to illustrate an advanced organic spin-interface architecture with thermal stability using graphene as a mediator between the metal molecules being affected and an underlying Cobalt layer. The research is a step toward innovating nanoscale systems and devices such as single molecular magnets.

Graphene is promising for a number of electronic applications at nanoscale. Formed into graphene nanoribbons (GNRs) it has been theorized they can be used for nanoscale light-emitting devices with controllable color emissions. But the experimental research is limited, and it has not integrated them into nanoscale electronic circuits with tunable color. Ferretti’s computational work on Marconi, along with experimentation, illustrated a novel approach to studying the electroluminescent properties of GNRs. “This research further confirms that atomic precision control of a structure integrated with a GNR can change the emission color of the ribbon. This opens the door to further research into production of GNR materials with predictable, tunable properties, whereby they can be functionally used in nanoscale circuits.”

These are only a few ways Marconi is helping to expand the world’s body of knowledge and bring insight into a host of scientific research. According to Cavazzoni, “at least 50 percent of Marconi is dedicated to larger European computational projects. A large part of that is in materials research, such as that of Doctor Ferretti. But, we also contribute to computational science in astrophysics, engineering, earth science—including weather and climate—high-energy physics, quantum chromodynamics (QCD), and bioinformatics. These are the main drivers of Marconi production.”

The next step for Cineca toward exascale is to add a 50 petaflops supercomputer in the next few years. That will enable even greater discoveries in the near future.

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!

TACC’s Upgraded Ranch Data Storage System Debuts New Features, Exabyte Potential

May 22, 2019

There's a joke attributed to comedian Steven Wright that goes, "You can't have everything. Where would you put it?" Users of advanced computing can likely relate to this. The exponential growth of data poses a steep c Read more…

By Jorge Salazar, TACC

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 abused term: transparency. Another surprise: HPE apparently Read more…

By Doug Black and Tiffany Trader

BlueField SmartNIC Backs Transformation to Bare Metal Kubernetes

May 21, 2019

Hardware vendors are betting the transition to 5G wireless networks supporting myriad connected consumer and industrial devices also will accelerate the shift to heavy-duty bare-metal servers as a way to provision cloud- Read more…

By George Leopold

HPE Extreme Performance Solutions

HPE and Intel® Omni-Path Architecture: How to Power a Cloud

Learn how HPE and Intel® Omni-Path Architecture provide critical infrastructure for leading Nordic HPC provider’s HPCFLOW cloud service.

For decades, HPE has been at the forefront of high-performance computing, and we’ve powered some of the fastest and most robust supercomputers in the world. Read more…

IBM Accelerated Insights

Smarter EDA: Leveraging New Technologies for Product Verification

There is perhaps no sector more competitive than the modern electronics industry. Macro-trends, including artificial intelligence, 5G, and the internet of things (IoT), continue to propel dramatic growth. Read more…

HPE to Acquire Cray for $1.3B

May 17, 2019

Venerable supercomputer pioneer Cray Inc. will be acquired by Hewlett Packard Enterprise for $1.3 billion under a definitive agreement announced this morning. The news follows HPE’s acquisition nearly three years ago o Read more…

By Doug Black & 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

HPE to Acquire Cray for $1.3B

May 17, 2019

Venerable supercomputer pioneer Cray Inc. will be acquired by Hewlett Packard Enterprise for $1.3 billion under a definitive agreement announced this morning. T Read more…

By Doug Black & Tiffany Trader

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

CCC Offers Draft 20-Year AI Roadmap; Seeks Comments

May 14, 2019

Artificial Intelligence in all its guises has captured much of the conversation in HPC and general computing today. The White House, DARPA, IARPA, and Departmen Read more…

By John Russell

Cascade Lake Shows Up to 84 Percent Gen-on-Gen Advantage on STAC Benchmarking

May 13, 2019

The Securities Technology Analysis Center (STAC) issued a report Friday comparing the performance of Intel's Cascade Lake processors with previous-gen Skylake u Read more…

By Tiffany Trader

Nvidia Claims 6000x Speed-Up for Stock Trading Backtest Benchmark

May 13, 2019

A stock trading backtesting algorithm used by hedge funds to simulate trading variants has received a massive, GPU-based performance boost, according to Nvidia, Read more…

By Doug Black

ASC19: NTHU Returns to Glory

May 11, 2019

As many of you Student Cluster Competition fanatics know by now, Taiwan’s National Tsing Hua University (NTHU) won the gold medal at the recently concluded AS Read more…

By Dan Olds

Intel 7nm GPU on Roadmap for 2021, OneAPI Coming This Year

May 8, 2019

At Intel's investor meeting today in Santa Clara, Calif., the company filled in details of its roadmap and product launch plans and sought to allay concerns about delays of its 10nm chips. In laying out its 10nm and 7nm timelines, Intel revealed that its first 7nm product would be... Read more…

By Tiffany Trader

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

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

ClusterVision in Bankruptcy, Fate Uncertain

February 13, 2019

ClusterVision, European HPC specialists that have built and installed over 20 Top500-ranked systems in their nearly 17-year history, appear to be in the midst o 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

Intel Reportedly in $6B Bid for Mellanox

January 30, 2019

The latest rumors and reports around an acquisition of Mellanox focus on Intel, which has reportedly offered a $6 billion bid for the high performance interconn Read more…

By Doug Black

Looking for Light Reading? NSF-backed ‘Comic Books’ Tackle Quantum Computing

January 28, 2019

Still baffled by quantum computing? How about turning to comic books (graphic novels for the well-read among you) for some clarity and a little humor on QC. The Read more…

By John Russell

The Case Against ‘The Case Against Quantum Computing’

January 9, 2019

It’s not easy to be a physicist. Richard Feynman (basically the Jimi Hendrix of physicists) once said: “The first principle is that you must not fool yourse Read more…

By Ben Criger

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

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

Deep500: ETH Researchers Introduce New Deep Learning Benchmark for HPC

February 5, 2019

ETH researchers have developed a new deep learning benchmarking environment – Deep500 – they say is “the first distributed and reproducible benchmarking s Read more…

By John Russell

IBM Bets $2B Seeking 1000X AI Hardware Performance Boost

February 7, 2019

For now, AI systems are mostly machine learning-based and “narrow” – powerful as they are by today's standards, they're limited to performing a few, narro Read more…

By Doug Black

Arm Unveils Neoverse N1 Platform with up to 128-Cores

February 20, 2019

Following on its Neoverse roadmap announcement last October, Arm today revealed its next-gen Neoverse microarchitecture with compute and throughput-optimized si Read more…

By Tiffany Trader

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

France to Deploy AI-Focused Supercomputer: Jean Zay

January 22, 2019

HPE announced today that it won the contract to build a supercomputer that will drive France’s AI and HPC efforts. The computer will be part of GENCI, the Fre Read more…

By Tiffany Trader

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

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