Julia is a high-performance open source programming language for numerical computing. 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 Dorilton Ventures-led Series A involving Menlo Ventures, General Catalyst and HighSage activities.
Analytics India Magazine met with CEO Viral Shah to gain insight into the company’s insides, current projects, co-pilot and future plans. “I am always proud to start the Julia Project while based in Bangalore and forming a significant community of contributors from India. Half of Julia’s Google Summer of Code students come from universities in India, ”he said.
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In addition to helping to develop one of the most popular numerical programming languages, Shah was actively involved in the Aadhaar project. He is also co-author (with Nandan Nilekani) of ‘Rebooting India’.
AIM: How has Julia developed over the years?
Viral Schaha: When the Julia programming language was developed 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 another that makes programming complex systems a more pleasant experience (like Python).
We asked a deceptively simple question: is it possible to create a single language that combines the simplicity of Python with the speed of C? The answer to this question is yes, and Julia keeps that promise and effectively solves the two language problems.
The Julia community released Julia 1.0 in 2018. Today Julia has been downloaded more than 29 million times. In addition, there are more than 6,000 packages, over 200,000 GitHub stars for Julia and her packages. More than 1,500 universities and more than 10,000 companies use Julia.
Julia is responsible for pharmaceutical modeling, risk analysis, planning space missions, optimizing school bus routes, cataloging space objects, protecting the power grid, flight safety, modeling human, animal, plant and genetic migration, calculating glacier ice thickness, robot movement, energy trading, dairy farming, macroeconomic Modeling, medical care by drone, medical diagnosis, autonomous driving and 3D printing.
AIM: How do you intend to use the previous funding?
Viral Schaha: Julia Computing plans to use the funding to hire engineers who have a vision and passion for reinventing technical computing. We will introduce a cloud product suite for industrial customers such as finance, pharma and energy. We’re also expanding our go-to-market team and looking for world-class sales and marketing executives. We provide our customers with the tools they need to be more productive, lower their compute costs, reduce their data center emissions, and get to market faster.
And all of that is built into Julia, the fastest and easiest high productivity language for scientific computing.
We are excited to have the support of leading venture firms such as Dorilton Ventures, Menlo Ventures and General Catalyst. Industry veteran Bob Muglia, former Snowflake CEO and former Microsoft President of Servers and Tools, has joined our board of directors.
AIM: What are the latest offerings from Julia Computing?
Viral Schaha: JuliaHub offers the power of a supercomputer available to every data scientist and engineer. It’s a cloud-based platform that allows users to write Julia programs and scale jobs from a single node to tens, hundreds, or thousands of CPUs. Julia is one of the few languages that runs natively on GPUs, and JuliaHub provides hassle-free access to GPUs. In addition to developing Julia-based programs, JuliaHub also makes it easy for users to leverage Julia’s cutting-edge scientific ML (or Sciml) skills 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 simulating electronic circuits combine traditional simulation with modern ML approaches to provide a user-friendly interface for technical users.
AIM: What are your plans with the JuliaHub platform?
Viral Schaha: JuliaHub is the fastest and easiest ramp to use Julia.
JuliaHub offers engineers, data scientists and innovators all the powerful computing power to implement their ideas on any scale.
JuliaHub can turn laptops into supercomputers and provide businesses with low resource consumption by providing speed, agility and smooth performance. In addition, JuliaHub offers effortless parallel computing with no infrastructure hurdles. It’s a secure platform with enterprise support.
Most importantly, JuliaHub enables the user to develop applications using a browser-based IDE, collaborate easily, and perform large computing tasks in the cloud. Cost forecasts are simple, transparent and immediately available – even before the project begins. Submitting an order is effortless and intuitive thanks to the clear user interface, which also offers dedicated space and tools for uploading and editing large data sets.
In collaboration with Pumas-AI, we make Pumas available on JuliaHub – an industry-leading package for pharmaceutical modeling and simulation.
AIM: What are your AI and machine learning offerings?
Viral Schaha: JuliaHub is a platform that makes it easy for Julia users to develop and scale their AI models with various deep learning packages such as Flux.jl, Knet.jl and the ecosystem of the models. Unsupervised algorithms are available through packages such as XGBoost.jl. What makes JuliaHub unique is that it also offers a range of domain-specific AI applications such as Pumas (Pharma), 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 planners to use AI and ML for multi-physics simulations – for example to make buildings more comfortable and energy efficient, to reduce emissions, to design batteries and to plan space missions. JuliaSPICE solves the two-language problem in circuit design.
AIM: How does the Julia language compare to Python and R, especially in data science applications?
Viral Schaha: Julia’s data science ecosystem has seen immense improvement and growth over the past two years. It started with carefully designed language support for missing data. On this basis, the JuliaData community has developed several high-quality and powerful packages such as CSV.jl (for loading data from CSV files), DataFrames.jl (for data processing and analysis), Arrow.jl (for interoperability) with the Arrow Ecosystem) and Tables.jl (a generic API for working with table data). The JuliaData package ecosystem offers functionality similar to R and Python, but often higher performance.
The benchmarks show that the Julia ecosystem is on par with R and Python and outperforms most of the most popular packages in other languages. Julia’s data science ecosystem benefits greatly from multithreading, as shown in The Great CSV Shodown. Julia’s Composable Multithreading enables user code and package code to be written in a multithreaded style that provides higher performance and enables terabyte-sized data sets to be processed on a single large server. Many use the large JuliaHub servers for precisely this purpose – you can easily use a hundred cores on a server with one terabyte of RAM. This scalability also means that Julia users don’t have to switch to Spark for extensive data analysis, as is usually done by Python and R users.
AIM: You said that technical computing is at a dead end today. Could you explain in more detail?
Viral Schaha: From a technical point of view, many of today’s numerical computing systems are stuck in a local basin of performance and ease of use. Today all common languages and technologies were developed at least three decades ago, regardless of whether Python, R, SAS or Matlab. As a result, the industry forced engineers and data scientists to use these languages for their prototyping and then rewrite their code in a low-level language like C ++. The bilingual problem dramatically reduces productivity, causes code to be handed over to a new team for production, and results in high costs and time to market – not to mention the carbon emissions from running slow programs on a large scale. Julia benefits from a much more modern design that solves the two-language problem.
The modern engineering successes at companies like Tesla and SpaceX are due to the fact that these companies use software much better than the incumbents. Julia Computing’s offerings provide such benefits for all industries (pharmaceuticals, energy, finance, medical, to name a few) and governments.
AIM: What do you think of GitHub Copilot?
Viral Schaha: Github copilot is fascinating. As it matures, it will likely reduce the boilerplate a programmer has to write, presumably by generating it automatically. my colleague Keno Fischer tweeted about his experience with copilot with Julia – and it’s pretty impressive.
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