Workshop on Program Synthesis for Scientific Computing (PSW)

The workshop report is now available in this web page: https://www.anl.gov/cels/program-synthesis-for-scientific-computing-report

The US Department of Energy is planning an aggressive research program as part of its AI initiative, and as highlighted in the recently-released AI for Science report (see section 9), program synthesis and other high-level, AI-integrated programming methods will potentially be an important part of this program. The workshop will explore opportunities to develop program synthesis techniques that apply to scientific programming.

Topics of Interest

The topics of interest include (but not limited to):

• Methods for injecting application requirements (e.g., natural-language descriptions, mathematical specifications, examples of desired behavior).

• Methods for searching the space of potentially-suitable programs (e.g., reinforcement learning and other machine-learning techniques, counter-example-driven refinement, evolutionary methods, optimization methods).

• Methods for verifying the correctness of potential programs or increasing the confidence of correctness in existing programs (including formal methods providing both absolute and statistical guarantees).

• Methods reflecting the multi-objective nature of these problems, including performance, power efficiency, verifiability, and understandability.

Goals

This workshop aims to gather experts in program synthesis together with experts in scientific application development in order to generate concrete ideas for future research. A summary of these ideas will be used to generate a report that will help inform DOE’s efforts in this space.

Registration and Format

There is no fee to attend the workshop. However, space is limited and registration is required. This event is a virtual workshop.

Dates

The workshop dates are August 4-5, 2020.

Keynote Speaker

Justin Gottschlich, Intel

Bio: Justin Gottschlich is a principal scientist and the director and founder of the Machine Programming Research group at Intel Labs. More generally, Justin guides the research and engineering of machine programming (MP) across Intel, which is a new pioneering research area for the company. Justin has a deep desire to build bridges with thought leaders across industry and academia to identify disruptive research and push it forward as a community. Recently he has been working with Amazon, Brown, Georgia Tech, Google AI, Hebrew University, IBM Research, Microsoft Research, MIT, Penn, Stanford, Texas A&M, UC-Berkeley, and UCLA, to name a few. He co-founded and is the principal investigator of the joint Intel/NSF CAPA research center which focuses on simplifying the software programmability challenge for heterogeneous hardware. He also helped create the ACM SIGPLAN Machine Learning and Programming Languages workshop and currently serves as its steering committee chair. Justin also has the distinguished honor of serving on the advisory board of Solar-Lezama et al.’s 2020 NSF Expeditions “Understanding the World Through Code.”

Machine Programming: Challenges and Opportunities

Abstract: As defined by “The Three Pillars of Machine Programming”, machine programming (MP) is concerned with the automation of software development. The three pillars partition MP into the following conceptual components: (i) intention, (ii) invention, and (iii) adaptation, with data being a foundational element that is generally necessary for all pillars. While the goal of MP is complete software automation – something that is likely decades away – we believe there are many seminal research opportunities waiting to be explored today across the three pillars.

In this talk, we will cover a diverse range of topics that we believe are central to the advancement of MP, including: (i) machine programming using approximate and precise methods, (ii) extraction of multi-dimensional and evolving code semantics, (iii) human-intended and machine-intended programming languages, (iv) novel structural representations of code, (v) intentional programming and behaviors, (vi) traditional and continuous learning for adaptive software (and hardware), (vii) automation for software and hardware heterogeneity, (viii) ethics of machine programming, and (ix) the future of data, communication, and computation for MP. For each topic, we will provide an abbreviated assessment of both the strengths and weaknesses of current research advances, mostly as a vehicle to stimulate active discussion during the workshop.

Agenda

Day 1 (Aug/4)

Day 2 (Aug/5)

Contact

Please address workshop questions to Hal Finkel (hfinkel@anl.gov) and/or Ignacio Laguna (ilaguna@llnl.gov). During the event, please also cc India Gordon (igordon@anl.gov).