We live in the age of Big Data, but most of that data is “sparse.” With sparse data, analytic algorithms end up doing a lot of addition and multiplication by zero, which is wasted computation. Programmers get around this by writing custom code to avoid zero entries, but that code is complex, and it generally applies only to a narrow range of problems. Researchers have developed a new system that automatically produces code optimized for sparse data. That code offers a 100-fold speedup over existing, non-optimized software packages. Also, its performance is comparable to that of meticulously hand-optimized code for specific sparse-data operations while requiring far less work on the programmer’s part. The system is called Taco, for tensor algebra compiler. In recent years, the mathematical manipulation of tensors — tensor algebra — has become crucial to big-data analysis and machine learning. It has been a staple of scientific research since Einstein’s time.
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