#computational software

Dongarra has led the world of high-performance computing through his contributions to efficient numerical algorithms for linear algebra operations, parallel computing programming mechanisms, and performance evaluation tools. For nearly forty years, Moore’s Law produced exponential growth in hardware performance. During that same time, while most software failed to keep pace with these hardware advances, high performance numerical software did—in large part due to Dongarra’s algorithms, optimization techniques, and production-quality software implementations.

These contributions laid a framework from which scientists and engineers made important discoveries and game-changing innovations in areas including big data analytics, healthcare, renewable energy, weather prediction, genomics, and economics, to name a few. Dongarra’s work also helped facilitate leapfrog advances in computer architecture and supported revolutions in computer graphics and deep learning.

Dongarra’s major contribution was in creating open-source software libraries and standards which employ linear algebra as an intermediate language that can be used by a wide variety of applications. These libraries have been written for single processors, parallel computers, multicore nodes, and multiple GPUs per node. Dongarra’s libraries also introduced many important innovations including autotuning, mixed precision arithmetic, and batch computations.

As a leading ambassador of high-performance computing, Dongarra led the field in persuading hardware vendors to optimize these methods, and software developers to target his open-source libraries in their work. Ultimately, these efforts resulted in linear algebra-based software libraries achieving nearly universal adoption for high performance scientific and engineering computation on machines ranging from laptops to the world’s fastest supercomputers. These libraries were essential in the growth of the field—allowing progressively more powerful computers to solve computationally challenging problems.

“Today’s fastest supercomputers draw headlines in the media and excite public interest by performing mind-boggling feats of a quadrillion calculations in a second,” explains ACM President Gabriele Kotsis. “But beyond the understandable interest in new records being broken, high performance computing has been a major instrument of scientific discovery. HPC innovations have also spilled over into many different areas of computing and moved our entire field forward. Jack Dongarra played a central part in directing the successful trajectory of this field. His trailblazing work stretches back to 1979, and he remains one of the foremost and actively engaged leaders in the HPC community. His career certainly exemplifies the Turing Award’s recognition of ‘major contributions of lasting importance.’”

[...]

For over four decades, Dongarra has been the primary implementor or principal investigator for many libraries such as LINPACK, BLAS, LAPACK, ScaLAPACK, PLASMA, MAGMA, and SLATE. These libraries have been written for single processors, parallel computers, multicore nodes, and multiple GPUs per node. His software libraries are used, practically universally, for high performance scientific and engineering computation on machines ranging from laptops to the world’s fastest supercomputers.

These libraries embody many deep technical innovations such as:

Autotuning: through his 2016 Supercomputing Conference Test of Time award-winning ATLAS project, Dongarra pioneered methods for automatically finding algorithmic parameters that produce linear algebra kernels of near-optimal efficiency, often outperforming vendor-supplied codes.

Mixed precision arithmetic: In his 2006 Supercomputing Conference paper, “Exploiting the Performance of 32 bit Floating Point Arithmetic in Obtaining 64 bit Accuracy,” Dongarra pioneered harnessing multiple precisions of floating-point arithmetic to deliver accurate solutions more quickly. This work has become instrumental in machine learning applications, as showcased recently in the HPL-AI benchmark, which achieved unprecedented levels of performance on the world’s top supercomputers.

Batch computations: Dongarra pioneered the paradigm of dividing computations of large dense matrices, which are commonly used in simulations, modeling, and data analysis, into many computations of smaller tasks over blocks that can be calculated independently and concurrently. Based on his 2016 paper, “Performance, design, and autotuning of batched GEMM for GPUs,” Dongarra led the development of the Batched BLAS Standard for such computations, and they also appear in the software libraries MAGMA and SLATE.