CLOVER: A Trifecta of Vendor-agnostic, GPU-accelerated Numerical Libraries

January 30, 2023

Jan. 30, 2023 — Numerical libraries have an enormous impact on scientific computing because they act as the gateway middleware that enables many applications to run on state-of-the-art hardware. Algorithmic and implementation advances in these libraries can increase the speed and accuracy of many modeling and simulation packages and can provide access to new computational systems.

Figure 1. Hartwig Anzt.

Part of the magic of mathematics is that the same mathematical methods can be applied to wildly disparate physical phenomena. Examples include linear algebra and the fast Fourier transform (FFT). This generality is the foundation upon which numerical libraries are built. A well-designed numerical library also provides an abstraction layer via an API in such a way that users need only focus on their research and not on the computational platform. Such is the case with the Computational Libraries Optimized via Exascale Research (CLOVER) project.

Encompassing three foundational libraries, the Exascale Computing Project’s (ECP’s) CLOVER team has brought GPU acceleration to the Software for Linear Algebra Targeting Exascale (SLATE) library for dense linear algebra, to Ginkgo for sparse linear algebra, and to the highly efficient FFTs for exascale (heFFTe) library for CPU- and GPU-accelerated, multinode, multidimensional FFTs. These libraries provide scientists around the world with access to the latest in GPU-accelerated computing—whether the application is running on an exascale system, on a computational cluster, or locally on a GPU-accelerated laptop or workstation.

Preliminary results demonstrate that CLOVER users can run efficiently on AMD, NVIDIA, and Intel GPU-accelerated systems. These benchmarks also demonstrate performance parity with AMD, NVIDIA, and Intel vendor library implementations on some computational kernels. In the race for performance portability, Hartwig Anzt (Figure 1)—director of the Innovative Computing Laboratory, professor in the Min H. Kao Department of Electrical Engineering and Computer Science (EECS) at the University of Tennessee, and research group leader at the Karlsruhe Institute of Technology—observed, “Software lives longer than hardware. Our team is working to avoid library death through good design.”

Scalability and performance are essential to achieving exascale performance. To this end, benchmark results demonstrate that the heFFTe library can scale to support production runs on the Frontier exascale system after it passes its ready-for-production acceptance tests. The heFFTe team conducted these benchmarks on Crusher, which is an early access test-bed system built with hardware identical to that of the Frontier supercomputer and designed with similar software.

Technology Introduction

Linear algebra provides scientists with a language to describe space and the manipulation of space by using numbers that can be calculated on a computer. Specifically, linear algebra is about performing linear combinations of operations, or linear transforms, by using arithmetic operations on columns of numbers (i.e., vectors) and arrays of numbers (i.e., matrices) to create new, more meaningful vectors and matrices. Based on such linear algebra calculations, scientists can glean useful information about speed, distance, or time in a physical space. Alternatively, scientists can use these calculations in an abstract space to perform a linear regression, which is a valuable tool used to predict data related to decision-making, medical diagnosis, and statistical inference. The Google PageRank algorithm used by search engines provides a common example of the power of linear algebra and eigenvalues. The 3Blue1Brown video “Essence of Linear Algebra Preview” briefly introduces linear algebra for a general technical audience.

Normally, the choice of technique and library depends on the type of matrix being targeted. For example, the SLATE library is designed to operate on dense matrices, in which most or all the elements in the matrix are nonzero. Arithmetic operations on these types of matrices tend to be computationally intensive, so users can expect higher performance on a GPU. The Ginkgo library, by contrast, is designed to operate on sparse matrices, in which few elements in the matrix are nonzero. Operations on sparse matrices tend to be bound by memory bandwidth  when accessing nonzero matrix elements and limited by memory capacity when an operation creates many nonzero matrix entries. Through smart design techniques, the Ginkgo team maximized the use of the GPU memory subsystem to achieve performance competitive with vendor-optimized, sparse matrix kernels. Ginkgo has a huge advantage over vendor libraries because it is GPU agnostic, so scientists are not locked into a specific vendor implementation or hardware.

The FFT has been described as “the most important numerical algorithm of our lifetime,” and the Institute of Electrical and Electronics Engineers (IEEE) magazine, Computing in Science & Engineering, included it in the top 10 algorithms of the twentieth century. It is used in many domain applications, including molecular dynamics, spectrum estimation, fast convolution and correlation, signal modulation, and wireless multimedia. The heFFTe website that more than a dozen Exascale Computing Project (ECP) applications use FFT in their codes. The 3Blue1Brown video, “But What Is the Fourier Transform? A Visual Introduction,” provides an overview of this important algorithm for a general audience.

The heFFTe library revisited the design of existing FFT libraries to develop a distributed, 3D FFT library and robust 2D implementations that can support applications that run on large-scale, heterogeneous systems with multicore processors and hardware accelerators. For example, the heFFTe team has focused on implementing a scalable, GPU-enabled, and performance-portable 3D FFT. Distributed 3D FFTs are one of the most important kernels in molecular-dynamics computations, and the performance of these FFTs can drastically affect an application’s ability to run on larger machines. Additionally, the redesign effort was a codesign activity that involved other ECP application developers. For more information, see “heFFTe—A Widely Applicable, CPU/GPU, Scalable Multidimensional FFT That Can Even Support Exascale Supercomputers.”

Anzt recognized the importance of lessons learned from the community-based Extreme-scale Scientific Software Development Kit (xSDK) and Extreme-scale Scientific Software Stack (E4S) projects. He emphasized that API design and user feedback go hand in hand. Furthermore, continuous integration (CI) is essential to ensure library performance and correctness on all supported systems. CI also frees development from the constraints of legacy algorithms and code decisions, as highlighted in the ECP article, “High-Accuracy, Exascale-Capable, Ab Initio Electronic Structure Calculations with QMCPACK: A Use Case of Good Software Practices.”

Technical Discussion

Each of the three libraries in the CLOVER project provides essential functionality for a large scientific user base. GPU acceleration is critical to achieving high performance on exascale supercomputers. Performance benchmarks demonstrate that the CLOVER libraries are ready for production runs on the Frontier exascale system. Early results on Intel and AMD GPUs demonstrate that all three libraries will deliver high performance regardless of the GPU vendor.

SLATE

The SLATE library implements a GPU-accelerated, distributed, dense linear algebra library that will replace the Scalable Linear Algebra PACKage (ScaLAPACK). The Linear Algebra PACKage (LAPACK) and ScaLAPACK have been the standard linear algebra libraries for decades, and this success can largely be attributed to the layered software stack (Figure 2) that can call vendor-optimized Basic Linear Algebra Subprograms (BLAS).

Figure 2. The SLATE software stack is designed in layers, thus enabling applications to call device-optimized linear algebra libraries.

SLATE follows this proven paradigm to implement similar ScaLAPACK functionality (e.g., parallel BLAS, norms, solving linear-systems, least squares, eigenvalue problems, singular value decomposition) and to expand coverage to new algorithms.

The goal is to support current hardware (i.e., CPUs and GPUs) and to provide sufficient flexibility to support future hardware designs. The software layers are designed to prevent language lock-in, as successfully demonstrated by current support for Open Multi-Processing (OpenMP), CUDA, ROCm (Radeon Open Compute Platform), and oneAPI. This performance portability is based on the C++ standard library and C++ templates to avoid code duplication.

 

To continue reading ECP’s report, please click here.


Source: Rob Farber, Exascale Computing Project

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!

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion XL — were added to the benchmark suite as MLPerf continues Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing power it brings to artificial intelligence.  Nvidia's DGX Read more…

Call for Participation in Workshop on Potential NSF CISE Quantum Initiative

March 26, 2024

Editor’s Note: Next month there will be a workshop to discuss what a quantum initiative led by NSF’s Computer, Information Science and Engineering (CISE) directorate could entail. The details are posted below in a Ca Read more…

Waseda U. Researchers Reports New Quantum Algorithm for Speeding Optimization

March 25, 2024

Optimization problems cover a wide range of applications and are often cited as good candidates for quantum computing. However, the execution time for constrained combinatorial optimization applications on quantum device Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at the network layer threatens to make bigger and brawnier pro Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HBM3E memory as well as the the ability to train 1 trillion pa Read more…

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing po Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HB Read more…

Nvidia Looks to Accelerate GenAI Adoption with NIM

March 19, 2024

Today at the GPU Technology Conference, Nvidia launched a new offering aimed at helping customers quickly deploy their generative AI applications in a secure, s Read more…

The Generative AI Future Is Now, Nvidia’s Huang Says

March 19, 2024

We are in the early days of a transformative shift in how business gets done thanks to the advent of generative AI, according to Nvidia CEO and cofounder Jensen Read more…

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

March 18, 2024

Nvidia's latest and fastest GPU, codenamed 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…

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…

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…

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…

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…

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…

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

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…

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…

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…

Intel Won’t Have a Xeon Max Chip with New Emerald Rapids CPU

December 14, 2023

As expected, Intel officially announced its 5th generation Xeon server chips codenamed Emerald Rapids at an event in New York City, where the focus was really o Read more…

IBM Quantum Summit: Two New QPUs, Upgraded Qiskit, 10-year Roadmap and More

December 4, 2023

IBM kicks off its annual Quantum Summit today and will announce a broad range of advances including its much-anticipated 1121-qubit Condor QPU, a smaller 133-qu Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire