Data vs. Instructions: Runtime Code Generation for Convolutions (Short Video)

Web Reference: Why Data Management Plans (DMPs) are required. The Belmont Forum and BiodivERsA support international transdisciplinary research with the goal of providing knowledge for understanding, mitigating and adapting to global environmental change. To meet this challenge, the Belmont Forum and BiodivERsA emphasize open sharing of research data to stimulate new approaches to the collection, analysis ... Why the Belmont Forum requires Data Management Plans (DMPs) The Belmont Forum supports international transdisciplinary research with the goal of providing knowledge for understanding, mitigating and adapting to global environmental change. To meet this challenge, the Belmont Forum emphasizes open sharing of research data to stimulate new approaches to the collection, analysis, validation and ... Access to data promotes reproducibility, prevents fraud and thereby builds trust in the research outcomes based on those data amongst decision- and policy-makers, in addition to the wider scientific community and the general public.

YouTube Excerpt: The short video presentation for CGO 2021 (International Symposium on Code Generation and Optimization) Student Research Competition. Just-in-time (JIT) code generation techniques are increasingly gaining traction due to their ability to significantly speedup computations. Small and medium matrix multiplication has wide uses in the domains of Machine Learning (ML) and scientific simulations in high performance computing. These computations can benefit greatly from JIT compilation. Prior runtime code generation approaches have primarily focused on loop unrolling and address compression techniques that focus on optimizing the instruction flow. In contrast, we consider a unique code generation approach where data values are dynamically embedded in instructions as immediate values, effectively transforming a memory load into an immediate load. By utilizing Intel’s AVX-512 vector extensions, we show that our technique achieves geometric mean speedups of 1.12× and 1.05× over MKL and MKL JIT, the current state of the art JIT library, for 32 input channels. We intend to open source our JIT library and plan to apply our approach to produce high performance convolution libraries.

The short video presentation for CGO 2021 (International Symposium on Code Generation and Optimization) Student Research Competition. Just-in-time...

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