Salt Lake City, Utah – Julia Computing and IBM presented Julia for Deep Learning at SC16, the world’s largest supercomputing conference held this week in Salt Lake City, Utah.
This exciting new technology combines IBM’s Power8 server platform and NVIDIA Tesla K80 GPU accelerators with the superior speed and performance of the Julia mathematical and scientific computing language. More details are available here.
Julia Computing and the IBM team used this powerful combination to apply deep learning to analyze medical images provided by Drishti Eye Hospitals to diagnose diabetic retinopathy, an eye disease that affects more than 126 million diabetics and accounts for more than 5% of blindness cases worldwide.
“India is home to 62 million diabetics,” explained Kiran Anandampillai, founder and CEO of Drishti Eye Hospitals, “many of whom live in rural areas with limited access to health facilities. Timely screening for changes in the retina can help get them to treatment and prevent vision loss. Julia Computing's work using deep learning makes retinal screening an activity that can be performed by a trained technician using a low cost fundus camera.”
According to Julia Computing CEO Viral Shah, “Using IBM’s Power platform with NVIDIA GPU accelerators increased processing speed by 57x – a dramatic improvement. IBM Power provides 2-3x more memory bandwidth combined with tight GPU accelerator integration to create a high performance environment for deep learning with Julia.”
Julia has the following key advantages for scientific and mathematical computing:
-
Julia is lightning fast. Julia provides speed improvements up to 1,000x for insurance model estimation, 225x for parallel supercomputing image analysis and 11x for macroeconomic modeling.
-
Julia is easy to learn. Julia’s flexible syntax is familiar and comfortable for users of Python and R.
-
Julia integrates well with existing code and platforms. Users of Python, R and other languages can easily integrate their existing code into Julia.
-
Elegant code. Julia was built from the ground up for mathematical, scientific and statistical computing, and has advanced libraries that make coding simple and fast, and dramatically reduce the number of lines of code required – in some cases, by 90% or more.
-
Julia solves the two language problem. Because Julia combines the ease of use and familiar syntax of Python and R with the speed of C, C++ or Java, programmers no longer need to estimate models in one language and reproduce them in a faster production language. This saves time and reduces error and cost.
Julia Computing was founded in 2015 by the co-creators of the Julia language to provide support to businesses and researchers who use Julia, the fastest modern open source programming language for data and analytics. Julia combines the functionality of quantitative environments such as Python and R with the speed of production programming languages like Java and C++ to solve big data and analytics problems. Julia delivers dramatic improvements in simplicity, speed, capacity and productivity.
Julia users and partners include: IBM, DARPA, Lawrence Berkeley National Laboratory, National Energy Research Scientific Computing Center (NERSC), Federal Aviation Administration (FAA), MIT Lincoln Labs, Intel, Moore Foundation, Nobel Laureate Thomas J. Sargent, Federal Reserve Bank of New York (FRBNY), Brazilian National Development Bank (BNDES), BlackRock, Conning, Berkery Noyes, BestX and many of the world's largest investment banks, asset managers, fund managers, foreign exchange analysts, insurers, hedge funds and regulators. Julia is being used to analyze images of the universe and research dark matter, drive parallel computing on supercomputers, diagnose medical conditions, manage 3D printers, build drones, improve air safety, provide analytics for foreign exchange trading, insurance, regulatory compliance, macroeconomic modeling, sports analytics, manufacturing and much, much more.
