Julia Programming’s Dramatic Rise in HPC and Elsewhere

By John Russell

January 14, 2020

Back in 2012 a paper by four computer scientists including Alan Edelman of MIT introduced Julia, A Fast Dynamic Language for Technical Computing. At the time, the gold standard programming languages for fast performance on computationally intensive problems were C and Fortran – maybe they still are. Fast forward to 2020 and Julia is making a run at the top and Edelman won last year’s IEEE Sidney Fernbach Award presented at SC19 for, among other things, his work on Julia.

Consider recent stats on Julia adoption. By January 1, 2019, reports Julialang.org, the total downloads of Julia reached 7.3 million. That number jumped to 12.9 million, a 77 percent increase, by January 1, 2020. The number of published citations for same period rose 66 percent from 1048 to 1680. In his SC19 talk, Edelman noted that as of October 2019 there were 3,119 Julia packages available, up from 1,688 at the year’s start. Those numbers are impressive all around.

In September, Julia joined the ranks of computing languages that have achieved peak performance exceeding one petaflop per second – the so-called ‘Petaflop Club.’ The Julia application that achieved this milestone is called Celeste. The Celeste team developed a new parallel computing method to process the entire Sloan Digital Sky Survey dataset and loaded an aggregate of 178 terabytes of image data to produce the most accurate catalog of 188 million astronomical objects in just 14.6 minutes. Celeste achieved peak performance of 1.54 petaflops using 1.3 million threads on 9,300 Knights Landing (KNL) nodes of the Cori supercomputer at NERSC – a performance improvement of 1,000x in single-threaded execution.

Alan Edelman, MIT

You get the idea. Julia is on a roll. Whether Julia will challenge Python the way Python once challenged and then surpassed Java is an interesting question being bandied about. What’s clear is that after percolating along steadily during its early years, Julia use is growing quickly – much to Edelman’s delight.

The tension between the high performance delivered by so-called static programming languages and the lesser performance delivered by high-level programming languages, which emphasize abstraction, speed of development, and portability, hasn’t gone away, noted Edelman. But convenience with sufficient performance is winning out. Moreover, the rise of heterogeneous computing and the complications it presents to programmers has increased the tilt away from static programming.

In his engaging SC19 talk, Edelman noted:

“When you’re writing various algorithms, you don’t necessarily want to think about whether you’re on a GPU, or whether you’re on a distributed computer. You don’t necessarily want to think about how you’ve implemented the specific data structure. What you want to do is talk about what you want to compute, not how you want to compute it, right? That is the big problem, to get people to talk about what you want to compute, and not how you want to compute it. Because if you put in your software, how you’re going to compute it, and if your software is filled with that muck, I promise you, nobody’s ever going to change it. No one’s going to innovate on it. When the person who wrote it is no longer in the project, no one’s ever going to touch it.

“[S]ome of the reasons why Julia is working very well is because we have particularly well-designed abstractions. We have something called multiple dispatch and we have a very careful balance between the static and dynamic. It interfaces with LLVM. It plays nicely with Python. We also have had lots of people take legacy codes in MPI, and plug them into Julia – you don’t get all the benefits, but what you do have, which might be the most important benefit, is other people can now run your code once it’s inside of Julia. So it’s much easier for other people. You can actually give your old code new life when you plug it into a higher level language.”

Julia, say advocates, minimizes performance penalties because it was designed from the outset with parallel computing in mind and with making use of high-performing abstractions able to exploit the latest libraries and deliver portability. Edelman presented an example in which a group of researchers decided to scrap their legacy climate code in Fortran and write it from scratch in Julia. There was some discussion around performance tradeoffs they might encounter in the move to a high level programming language. The group was willing to accept a 3x slowdown for the flexibility of the language. Instead, said Edelman, the switch produced 3x speedup.

 

He briefly presented a second example in which Julia was used with GPUs and skirted CUDA.

“I’m going to go over really fast a little bit on how we do Julia on GPUs. Because we have these different levels of abstraction, we’re able to reason about what’s going on at various different levels. If you only have that very lowest level on a GPU again, you can roll up your sleeves and work really hard, but you don’t get any code reuse. We have the saying in the Julia world, where if you’ve copied and pasted code, and you just modified a few things, then you’ve done something wrong. We’re trying to eliminate the copying and pasting the code, not only by you, but somebody else in the community shouldn’t have to go and take somebody else’s code and copy and paste it. That shouldn’t be necessary. So there’s a lot going on in here (see slide below). But the main point is that it’s not just queued in Julia, but it’s actually Julia running on the GPUs. And so that’s, that’s pretty exciting.”

Edelman’s perspective on coding and mathematics is interesting. “You know, a lot of us get this impression from universities and from teaching that you learn some math and then you [build] an algorithm, and then you code it up as if there is the algorithm first, and then the coding is sort of the secondary. But you know, more and more now, the code is the math,” said Edelman. This idea, he suggests, should inform our thinking about coding generally; it’s yet another effective abstraction.

The video is best watched (or listened to) to get the breezy yet substantive flavor of Edelman’s ideas and Julia’s capabilities.

“Julia was always designed to be a high level of parallel computing language, even from day one. That’s what I wanted. This is the problem that I personally wanted to see solved. We’re not fully there yet. But Julia is a highlight. You could do distributed computing, you could do GPU computing, you could do shared memory computing. We have models, you know, asynchronous computing, whatever you’d like to do we have models to do it now. And the real question, the one that everybody asked, the one that none of us really knows how to do, the deep intellectual problem is how to put it all together. But if we all work together at it, if we actually all share code, and, you know, hammer away at it, I think we could actually solve this problem.”

Link to intro paper: https://arxiv.org/pdf/1209.5145.pdf

Link to Fernbach Award announcement: https://www.hpcwire.com/off-the-wire/julia-computing-chief-scientist-alan-edelman-wins-prestigious-ieee-sidney-fernbach-award/

Link to SC19 video: https://www.youtube.com/watch?v=nwdGsz4rc3Q

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