Jeremy-Howard
Jeremy Howard demonstrated the capabilities of the new language Mojo, which is a super set of Python, showing how it solves the issue of writing concise, flexible and efficient code. He started with matrix multiplication, demonstrating how with a struct, feature vectorization, parallelization, and tiling it was possible to achieve a speed-up of 2200 times over Python. This is built into Mojo to enable users without programming or hardware expertise to optimize and accelerate computations. With Mojo, it's easy to create an auto-tuned version of a program, and compute the Mandelbrot set 35,000 times faster than Python, making it an ideal platform for writing neural networks.
In this section, Jeremy Howard demonstrated how the new language, Mojo, solves the problem he has been complaining about for years. Using a notebook in Mojo, which is a super set of Python, Howard showed how he can write concise, flexible, and performant code. As a demonstration, he started with matrix multiplication, one of the fundamental algorithms in deep learning, and showed how easy it is to write efficient code in Mojo. By writing a struct to create a nice, compact, and memory-optimized matrix, Howard was able to achieve an 8.5 times speed-up. Further optimizations using feature vectorization, parallelization, and tiling resulted in a 2200 times speed-up over Python, and these optimizations were all built into Mojo so that developers don't have to worry about different versions, memory, and CPUs.
In this section, the presenter demonstrates how the Mojo launch will enable users to easily optimize and accelerate complex computations without needing to have deep hardware knowledge or programming expertise. Examples are shown, such as creating an auto-tuned version of a program and computing the Mandelbrot set 35,000 faster than Python, with the use of Mojo. The presenter concludes that Mojo is flexible, fast, and easy to understand for programmers of all levels, making it an ideal platform for writing neural networks.
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