Interview with Viral Shah, co-founder and CEO of Julia Computing

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Julia is a high-level, high-performance, open source programming language for numerical computation. Alan Edelman, Jeff Bezanson, Stefan Karpinski, Keno Fischer and Deepak Vinchhi and Viral Shah founded Julia Computing in 2015. In July, the company raised $ 24 million in Series A led by Dorilton Ventures, with participation from Menlo Ventures, General Catalyst and HighSage. Adventures.

Analytics India Magazine met the CEO Viral shah to get an overview of the internal functioning of the company, current projects, the co-pilot and future plans. “I am always proud to have started Project Julia while I was based in Bangalore and to form a large community of contributors in India. Half of Julia’s Google Summer of Code students come from Indian universities, ”he said.

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In addition to co-developing one of the most popular digital programming languages, Shah has been actively involved in the Aadhaar project. He is also the co-author (with Nandan Nilekani) of ‘Rebooting India’.

Extracts:

AIM: How has Julia evolved over the years?

Viral shah: When the Julia programming language was designed at MIT in 2009 to solve a problem that still exists for non-Julia users: the need to use two (or more) languages, one for speed (often C or C ++) and one that makes programming complex systems a more enjoyable experience (like Python).

We asked a deceptively simple question: is it possible to create a unique language that combines the simplicity of Python with the speed of C? The answer to this question is yes, and Julia keeps that promise, effectively solving both language issues.

Julia community freed Julia 1.0 in 2018. Today, Julia has been downloaded over 29 million times. In addition, there are over 6,000 packages, over 200,000 GitHub stars for Julia and her packages. Over 1,500 universities and over 10,000 businesses use Julia.

Julia is used for pharmaceutical modeling, risk analysis, space mission planning, school bus route optimization, deep space object cataloging, power grid protection, aviation safety, modeling of human, animal, plant and genetic migration, calculation of the thickness of glacial ice, robot locomotion, energy trading, dairy farming, macroeconomic modeling, delivery of medical supplies by drone, medical diagnosis, autonomous driving and 3D printing.

OBJECTIVE: How do you plan to use recent funding?

Viral shah: Julia Computing plans to use the funding to hire engineers who have a vision and a passion to reinvent technical computing. We will deploy a suite of cloud products for industrial customers such as finance, pharmaceuticals and energy. We are also expanding our go-to-market team and looking for world-class leaders in sales and marketing. We provide our customers with the tools they need to be more productive, lower their compute costs, lower their data center emissions and get to market faster.

And it’s all built into Julia, the fastest and easiest high productivity language for scientific computing.

We are delighted to have the support of top venture capital firms such as Dorilton Ventures, Menlo Ventures and General Catalyst. Industry veteran Bob Muglia, former CEO of Snowflake and former President of Microsoft Servers and Tools, has joined our board of directors.

AIM: What are the latest offers from Julia Computing?

Viral shah: JuliaHub brings supercomputer power to the fingertips of every data scientist and engineer. It is a cloud-based platform that allows users to write Julia programs and scale tasks from a single node to tens, hundreds, or thousands of processors. Julia is one of the only languages ​​to run natively on GPUs, and JuliaHub provides effortless access to GPUs. In addition to developing Julia-based programs, JuliaHub also makes it easy for users to harness Julia’s advanced scientific capabilities in ML (or sciml) and access various industry-specific applications. Products such as Pumas for pharmaceutical modeling and simulation, JuliaSim for multi-physics modeling and simulation, and JuliaSPICE for the simulation of electronic circuits combine traditional simulation with modern ML approaches to provide an easy to use interface for technical users.

OBJECTIVE: What are your projects with the JuliaHub platform?

Viral shah: JuliaHub is the fastest and easiest ramp to take advantage of Julia.

JuliaHub gives engineers, data scientists and innovators all the high-performance computing power to implement their ideas at any scale.

JuliaHub can turn laptops into supercomputers and keep businesses light, delivering speed, agility and frictionless performance. Additionally, JuliaHub offers effortless parallel computing without infrastructure hurdles. It is a secure platform with enterprise support.

Most importantly, JuliaHub allows the user to develop applications with a browser-based IDE, easily collaborate, and perform large compute tasks in the cloud. Cost projections are simple, transparent and available immediately, even before the start of the project. Submitting a job is simple and intuitive, thanks to the clean interface that also offers dedicated space and tools for uploading and manipulating large data sets.

See also

In partnership with Pumas-AI, we offer Pumas on JuliaHub, a state-of-the-art software package for pharmaceutical modeling and simulation.

AIM: What are your AI and machine learning offerings?

Viral shah: JuliaHub is a platform that allows Julia users to easily develop and scale their AI models using various deep learning packages such as Flux.jl, Knet.jl and the ecosystem of models. Unsupervised algorithms are available through packages such as XGBoost.jl. What makes JuliaHub unique is that it also provides a suite of domain-specific AI applications, such as Pumas (pharmaceuticals), JuliaSim (engineering), and JuliaSPICE (circuit design). Pumas makes it easy for pharmaceutical researchers to use AI and ML techniques to identify promising new drugs, predict toxicity, identify optimal dosage, and more. JuliaSim enables building designers to use AI and ML for multi-physics simulations, such as making buildings more comfortable and energy efficient, reducing emissions, designing batteries and planning space missions. JuliaSPICE solves the two-language problem in circuit design.

OBJECTIVE: How does the Julia language compare to Python and R, especially in data science applications?

Viral shah: Julia’s data science ecosystem has seen tremendous improvement and growth over the past two years. It all started with carefully designed language support for missing data. Based on this, the JuliaData community has built several high quality and performing packages such as CSV.jl (to load data from CSV files), DataFrames.jl (for data manipulation and analysis), Arrow.jl (to interact with the Arrow ecosystem), and Tables.jl (a generic API for working with tabular data). The JuliaData package ecosystem offers functionality similar to R and Python, but often superior performance.

the landmarks demonstrate that the Julia ecosystem is comparable to R and Python and offers superior performance than most packages commonly used in other languages. Julia’s data science ecosystem greatly benefits from multi-threading, as demonstrated in The great CSV game. Julia’s composable multithreaded allows user code and package code to be written in a multithreaded style that provides higher performance and can handle terabyte-sized datasets on a single large server. Many use JuliaHub’s large servers for precisely this purpose – you can easily squeeze a hundred cores on a server with a terabyte of RAM. This level of scalability also means that Julia users do not need to switch to Spark for deep data analysis, as is typically done by Python and R users.

OBJECTIVE: You said technical computing is stuck in a rut today. Could you detail?

Viral shah: From a technical standpoint, many current digital computing systems are stuck in a local pool of performance and ease of use. Today, all commonly used languages ​​and technologies were designed at least three decades ago, be it Python, R, SAS or Matlab. Thus, the industry has forced engineers and data scientists to use these languages ​​for their prototyping and then to rewrite their code in a low-level language such as C ++. The two-language problem severely limits productivity, results in code being transferred to a new production team, and results in high costs and time-to-market, not to mention the CO2 emissions resulting from running slow-to-market programs. large scale. Julia benefits from a much more modern design that solves the problem of two languages.

The successes of modern engineering at companies like Tesla and SpaceX happened because these companies exploited software much better than incumbents. Julia Computing’s offerings provide such benefits to all industries (pharmaceutical, energy, finance, medicine, to name a few) and governments.

OBJECTIVE: What is your opinion on GitHub Copilot?

Viral shah: Github Copilot is fascinating. As it matures, it is likely to reduce the master key that a programmer has to write, possibly by generating it automatically. My colleague Keno Fischer tweeted about his experiences using Copilot with Julia – and that’s pretty impressive.


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