About Bodo

We Deal With Your Data Issues so You Can Solve the Important Problems

Bodo can be translated into “Run” or “Accelerate”, and our technology does just that. For developers and data scientists who regularly perform large-scale analytics and related applications, Bodo offers a parallel data compute platform providing extreme scale and speed, but with the simplicity and flexibility of native Python. In contrast to using Python libraries and frameworks, Bodo is a new type of inferential compiler offering true parallelism and high efficiency surpassing 10,000+ cores.

By improving analytics performance by 100x-1,000x, runtime efficiency by 10x, decreasing learning curves to 1/10, and decreasing aggregate operational to 1/10, new business possibilities suddenly arise. Broad access to such inexpensive near-real-time analytics enables new data-centric revenue opportunities, faster competitive responses, and radically more efficient overall data operations.

Our Beliefs

We exist to change the face of analytics computing; to accelerate access to performance, scale, and simplicity for data-intensive applications.

Bodo is the first extreme-performance Python data analytics platform. It is the culmination of years of research in academia and industry, from the creators of the first auto-parallelization compiler technology. This technology has long been a “holy grail” for researchers working on solving the gap between simplicity and performance in computing. By democratizing access to parallel computing, we aim to accelerate data insights, ML and AI in ways the industry can’t yet imagine.

Parallel computing is the only way to extend Moore’s Law for compute performance.

Most authorities agree that Moore’s law is being challenged. Data sets continue to grow, driven principally by AI and ML. In order to keep pace with compute demands, we at Bodo believe that taking advantage of parallelization, combined with today’s abundant cloud CPU resources, is the best answer. With the advent of mega-cloud vendors bringing cost-effective access to this processing power, combining with HPC-style parallelization to this infrastructure will again accelerate computing performance and access to computing speed.

Data analytics developers work under a massive simplicity vs. performance gap. Parallel computing must be simplified/democratized for their use

Today’s data scientists and developers face daunting learning curves, glue-code, and special-purpose hardware/software combinations. Achieving extreme performance has therefore meant giving up programming simplicity and velocity. Bodo instead believes that extreme performance can be achieved and made accessible to everyday developers through the use of inferential, auto-parallelization compilation which is far more accessible to the developer.

Existing high-performance programming workarounds - like libraries and frameworks - fail to address the underlying compute scaling issues.

Open Source communities, and the software industry overall, has generated a plethora of add-in libraries and frameworks to enable scale-out programming for developers and data scientists. However, none of these solutions has brought true linear scaling, and all require some new APIs and/or steep learning curves. They have all failed to consider a deeper look at parallel programming fundamentals, and have overlooked considering JIT compilation as a solution. The inefficiencies of the underlying approach drive the complexity of the systems and features that most solutions offer today. In contrast, Bodo adheres to the most advanced parallel computing principles, while making it accessible to programmers so they can focus on solving the problems that matter.

Python must be considered a “first-class” default platform

Python has been viewed by the industry as just a scripting prototype language. But for production, code would be rewritten in C++ or Java/Scala for performance and reliability. Automatic parallelization, code optimization, full type checking, and native code generation will now bring C++/MPI levels of speed, reliability and scalability. This paradigm shift will finally enable rapid development velocity that was hard to imagine previously.

Our Leaders

Behzad Nasre

CEO and Co-Founder

Behzad is a global business leader with years of executive leadership experience and deep subject matter expertise in business strategy, development, strategic planning, and general management.

Behzad's vision is to change the face of analytics computing by democratizing access to performance, scale, and simplicity for data-intensive analytics. By doing so, he aims to help unlock the value of real-time data analytics, as well as unleash the talent of data scientists everywhere.

In addition to leading Bodo, Behzad also serves as advisor/investor at multiple early stage companies, and was previously Vice President of Business Development at Endura, as well as Managing Director of Business Development with Intel Capital.

Behzad received his BS and MS in Electrical Engineering, from Cornell University.

Ehsan Totoni

CTO and Co-Founder

Ehsan is an entrepreneur, computer science researcher, and software engineer working on democratization of High-Performance Computing (HPC) for data analytics/AI/ML. Ehsan received his PhD in computer science from the University of Illinois at Urbana-Champaign, working on various aspects of HPC and Parallel Computing. He then worked as a research scientist at Intel Labs and Carnegie Mellon University, focusing on programming systems to address the gap between programmer productivity and computing performance.

Ehsan drives Bodo’s mission to democratize HPC for data science, with a belief that it should not remain the bastion of a select few experts. And, as Moore’s Law begins to slow, it is Ehsan’s vision to apply parallel computing principles to maintain computing’s evolution.

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