Analog computers are systems that perform computations by manipulating physical quantities such as electrical current, that map math variables, instead of representing information using abstraction ...

Discovering faster algorithms for matrix multiplication remains a key pursuit in computer science and numerical linear algebra. Since the pioneering contributions of Strassen and Winograd in the late ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of computing a matrix inverse using the Newton iteration algorithm. Compared to other algorithms, Newton ...

The Nature Index 2025 Research Leaders — previously known as Annual Tables — reveal the leading institutions and countries/territories in the natural and health sciences, according to their output in ...

Provide Purdue faculty, staff, and students with a single source summary of URE programs. Give UR programs, administrators, and mentors broader and inclusive marketing to prospective student ...

Parallel Computing starter project to build GPU & CPU kernels in CUDA & C++ and call them from Python without a single line of CMake using PyBind11 ...

Large language models such as ChaptGPT have proven to be able to produce remarkably intelligent results, but the energy and monetary costs associated with running these massive algorithms is sky high.

Is there any way to perform batched matrix multiplication within a program instance? For example, within a program I might load two tensors with shapes (8, 16, 16) and (8, 16, 16). The batch size is 8 ...

Old Glories: Fortran and Cobol are still among the world's most popular programming languages despite being almost 70 years old. They're certainly overachieving, but for entirely different reasons, ...