Adapting Algorithms to Modern Hybrid Architectures

By Tiffany Trader

August 13, 2014

Technology, like other facets of life, commonly experiences cycles of rapid change followed by periods of relative stability. Computing has entered a stage of increased architectural diversity, as evidenced by the rise of accelerators, coprocessors, and other alternatives, like ARM processors. An international team of researchers explores how these various supercomputing architectures perform on parallelized turbulent flow problems.

In their paper “Direct Numerical Simulation of Turbulent Flows with Parallel Algorithms for Various Computing Architectures,” the authors describe the process of creating efficient parallel algorithms for large-scale simulations of turbulent flows and comparing their performance on AMD, NVIDIA and Intel Xeon Phi parts. They also introduce a new series of direct numerical simulations of incompressible turbulent flows with heat transfer performed with the newly-developed algorithms.

The authors classify modern supercomputers into three categories:

1. Classical ones that run on computing power of central processing units (CPU)
2. Hybrid machines with CPUs and graphics processing units (GPU)
3. Hybrid machines with CPUs and Intel Xeon Phi accelerators of many integrated core (MIC) architecture.

To optimize performance, algorithms need to be customized for each system type.

“The first type, the basic one, requires highly scalable parallel algorithms that can run on thousands of cores,” the authors state. “It also needs efficient shared-memory parallelization with large number of threads to engage modern multi-core nodes: two 12-core Intel Xeon CPUs with Hyper Threading (HT) can execute 48 parallel threads on a dual-CPU node. In addition it needs efficient vectorization since AVX extension operates with vectors of 4 doubles. The second type requires adaptation of algorithms to the streaming processing which is a simplified form of parallel processing related with SIMD (single instruction multiple data) model. This can be a challenge itself. The third type requires much more deep multi-threaded parallelism and vectorization than the first type.”

There is also a fourth type, ARM-based architectures, which like other hybrid types, involve a lot of attention to optimize memory access and load balancing between the CPU and accelerators. However, the main focus of this paper is on GPGPUs from NVIDIA and AMD and on the Intel Phi coprocessor.

The team take a multilevel approach that combines different parallel models. They explain: “MPI is used on the first level within the distributed memory model to couple computing nodes of a supercomputer. On the second level OpenMP is used to engage multi-core CPUs and/or Intel Xeon Phi accelerators. The third level exploits the computing potential of massively-parallel accelerators.”

OpenMP and OpenCL-based extensions were developed to exploit the computing potential of modern hybrid machines. In adapting the computational algorithms to different accelerator architectures, the group came across some interesting findings regarding performance.

WCCM Mesh Figure 3
Figure 3: Comparison of performance on a mesh with 472114 cells (flow around a sphere) for different devices using a 1st order finite-volume scheme for unstructured meshes

WCCM Mesh Figure 4
Figure 4: Comparison of performance on a mesh with 679339 cells (flow around a sphere) for different devices using a 2nd order polynomial-based finite-volume scheme for unstructured meshes

Looking at figure 3 and 4 (above) the team stated “it can be noted that for the 1st order scheme (Figure 3) NVIDIA GTX TITAN outperforms AMD 7970 while for the 2nd order polynomial-based scheme which requires much more resources (registers and shared memory usage) AMD one significantly outperforms NVIDIA one. This indicates the insufficiency of register and local memory of NVIDIA architecture that prevents from achieving high occupancy of the device and reduces efficiency.”

Also in Figure 4, it can be seen that the Intel Xeon Phi architecture is less performant than the various GPUs. Although this could be due to the OpenCL implementation, an OpenMP implementations resulted in similar behavior, providing only a 10-20 percent speedup over an 8-core Intel Xeon E5-2690 CPU.

“So the common statement that Intel Xeon Phi is much easier to use than GPU because it can handle the same CPU code is an illusion,” they conclude. “The computing power of this kind of accelerator is much more difficult to get.”

Structured and unstructured mesh algorithms modified for significantly multithreaded OpenMP parallelization demonstrated high internal speedups: up to 200 times faster on Intel Xeon Phi compared to a sequential execution on the same accelerator. However, net performance was not much higher than an 8-core CPU. Surprised by this result, the team speculates it could be related to insufficient memory latency hiding mechanisms that are based on 4-thread hyper threading. A GPU, they note, can have tens of threads switching for latency hiding. They add that poor cache performance could also be a contributing factor.

The paper serves as another reminder that system architectures must be assessed in the context of a specific workloads. For the OpenCL kernels of the algorithm on unstructured meshes, “the different GPUs considered substantially outperform Intel Xeon Phi accelerator,” the team concludes, adding, “the AMD GPU tends to be more efficient than NVIDIA on heavy computing kernels.”

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industry updates delivered to you every week!

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, code-named Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from its predecessors, including the red-hot H100 and A100 GPUs. Read more…

Nvidia Showcases Quantum Cloud, Expanding Quantum Portfolio at GTC24

March 18, 2024

Nvidia’s barrage of quantum news at GTC24 this week includes new products, signature collaborations, and a new Nvidia Quantum Cloud for quantum developers. While Nvidia may not spring to mind when thinking of the quant Read more…

2024 Winter Classic: Meet the HPE Mentors

March 18, 2024

The latest installment of the 2024 Winter Classic Studio Update Show features our interview with the HPE mentor team who introduced our student teams to the joys (and potential sorrows) of the HPL (LINPACK) and accompany Read more…

Houston We Have a Solution: Addressing the HPC and Tech Talent Gap

March 15, 2024

Generations of Houstonian teachers, counselors, and parents have either worked in the aerospace industry or know people who do - the prospect of entering the field was normalized for boys in 1969 when the Apollo 11 missi Read more…

Apple Buys DarwinAI Deepening its AI Push According to Report

March 14, 2024

Apple has purchased Canadian AI startup DarwinAI according to a Bloomberg report today. Apparently the deal was done early this year but still hasn’t been publicly announced according to the report. Apple is preparing Read more…

Survey of Rapid Training Methods for Neural Networks

March 14, 2024

Artificial neural networks are computing systems with interconnected layers that process and learn from data. During training, neural networks utilize optimization algorithms to iteratively refine their parameters until Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, code-named Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from Read more…

Nvidia Showcases Quantum Cloud, Expanding Quantum Portfolio at GTC24

March 18, 2024

Nvidia’s barrage of quantum news at GTC24 this week includes new products, signature collaborations, and a new Nvidia Quantum Cloud for quantum developers. Wh Read more…

Houston We Have a Solution: Addressing the HPC and Tech Talent Gap

March 15, 2024

Generations of Houstonian teachers, counselors, and parents have either worked in the aerospace industry or know people who do - the prospect of entering the fi Read more…

Survey of Rapid Training Methods for Neural Networks

March 14, 2024

Artificial neural networks are computing systems with interconnected layers that process and learn from data. During training, neural networks utilize optimizat Read more…

PASQAL Issues Roadmap to 10,000 Qubits in 2026 and Fault Tolerance in 2028

March 13, 2024

Paris-based PASQAL, a developer of neutral atom-based quantum computers, yesterday issued a roadmap for delivering systems with 10,000 physical qubits in 2026 a Read more…

India Is an AI Powerhouse Waiting to Happen, but Challenges Await

March 12, 2024

The Indian government is pushing full speed ahead to make the country an attractive technology base, especially in the hot fields of AI and semiconductors, but Read more…

Charles Tahan Exits National Quantum Coordination Office

March 12, 2024

(March 1, 2024) My first official day at the White House Office of Science and Technology Policy (OSTP) was June 15, 2020, during the depths of the COVID-19 loc Read more…

AI Bias In the Spotlight On International Women’s Day

March 11, 2024

What impact does AI bias have on women and girls? What can people do to increase female participation in the AI field? These are some of the questions the tech Read more…

Alibaba Shuts Down its Quantum Computing Effort

November 30, 2023

In case you missed it, China’s e-commerce giant Alibaba has shut down its quantum computing research effort. It’s not entirely clear what drove the change. Read more…

Nvidia H100: Are 550,000 GPUs Enough for This Year?

August 17, 2023

The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…

Analyst Panel Says Take the Quantum Computing Plunge Now…

November 27, 2023

Should you start exploring quantum computing? Yes, said a panel of analysts convened at Tabor Communications HPC and AI on Wall Street conference earlier this y Read more…

DoD Takes a Long View of Quantum Computing

December 19, 2023

Given the large sums tied to expensive weapon systems – think $100-million-plus per F-35 fighter – it’s easy to forget the U.S. Department of Defense is a Read more…

Shutterstock 1285747942

AMD’s Horsepower-packed MI300X GPU Beats Nvidia’s Upcoming H200

December 7, 2023

AMD and Nvidia are locked in an AI performance battle – much like the gaming GPU performance clash the companies have waged for decades. AMD has claimed it Read more…

Synopsys Eats Ansys: Does HPC Get Indigestion?

February 8, 2024

Recently, it was announced that Synopsys is buying HPC tool developer Ansys. Started in Pittsburgh, Pa., in 1970 as Swanson Analysis Systems, Inc. (SASI) by John Swanson (and eventually renamed), Ansys serves the CAE (Computer Aided Engineering)/multiphysics engineering simulation market. Read more…

Intel’s Server and PC Chip Development Will Blur After 2025

January 15, 2024

Intel's dealing with much more than chip rivals breathing down its neck; it is simultaneously integrating a bevy of new technologies such as chiplets, artificia Read more…

Baidu Exits Quantum, Closely Following Alibaba’s Earlier Move

January 5, 2024

Reuters reported this week that Baidu, China’s giant e-commerce and services provider, is exiting the quantum computing development arena. Reuters reported � Read more…

Leading Solution Providers

Contributors

Choosing the Right GPU for LLM Inference and Training

December 11, 2023

Accelerating the training and inference processes of deep learning models is crucial for unleashing their true potential and NVIDIA GPUs have emerged as a game- Read more…

Training of 1-Trillion Parameter Scientific AI Begins

November 13, 2023

A US national lab has started training a massive AI brain that could ultimately become the must-have computing resource for scientific researchers. Argonne N Read more…

Shutterstock 1179408610

Google Addresses the Mysteries of Its Hypercomputer 

December 28, 2023

When Google launched its Hypercomputer earlier this month (December 2023), the first reaction was, "Say what?" It turns out that the Hypercomputer is Google's t Read more…

Comparing NVIDIA A100 and NVIDIA L40S: Which GPU is Ideal for AI and Graphics-Intensive Workloads?

October 30, 2023

With long lead times for the NVIDIA H100 and A100 GPUs, many organizations are looking at the new NVIDIA L40S GPU, which it’s a new GPU optimized for AI and g Read more…

AMD MI3000A

How AMD May Get Across the CUDA Moat

October 5, 2023

When discussing GenAI, the term "GPU" almost always enters the conversation and the topic often moves toward performance and access. Interestingly, the word "GPU" is assumed to mean "Nvidia" products. (As an aside, the popular Nvidia hardware used in GenAI are not technically... Read more…

Shutterstock 1606064203

Meta’s Zuckerberg Puts Its AI Future in the Hands of 600,000 GPUs

January 25, 2024

In under two minutes, Meta's CEO, Mark Zuckerberg, laid out the company's AI plans, which included a plan to build an artificial intelligence system with the eq Read more…

Google Introduces ‘Hypercomputer’ to Its AI Infrastructure

December 11, 2023

Google ran out of monikers to describe its new AI system released on December 7. Supercomputer perhaps wasn't an apt description, so it settled on Hypercomputer Read more…

China Is All In on a RISC-V Future

January 8, 2024

The state of RISC-V in China was discussed in a recent report released by the Jamestown Foundation, a Washington, D.C.-based think tank. The report, entitled "E Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire