PACT 2022   October 10–12, 2022

Tutorials/Workshops Program

🚧 Please be aware that the program shown here is provisional and still subject to change. 🚧

  • DPI below refers to the Discovery Partners Institute, on the fourth floor of 200 S. Wacker Drive, Chicago, IL.
  • IC below refers to the Illini Center, on the 19th floor of 200 S. Wacker Drive, Chicago, IL.

Saturday, October 8, 2022

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Time What Where
8:00–9:00 Continental Breakfast Alma Mater Room, IC
9:00–10:30 Tutorial: Memory-Centric Computing pt. 1 Orange & Blue Room, IC
10:30–11:00 Coffee Break Alma Mater Room, IC
11:00–12:30 Tutorials / Workshops Resume (locations as above)
12:30–14:00 Lunch Alma Mater Room, IC
14:15–15:45 Tutorial: Memory-Centric Computing pt. 2 Orange & Blue Room, IC
14:15–15:45 Tutorial: SHAD C++ Library Discovery Room, DPI
14:15–15:45 Tutorial: NVMExplorer Classroom A, DPI
15:45–16:15 Coffee Break Classroom B, DPI
16:15–17:45 Tutorials / Workshops Resume (locations as above)

Tutorial: Memory-Centric Computing
  • Onur Mutlu (ETH Zurich)

Computing is bottlenecked by data. Large amounts of application data overwhelm storage capability, communication capability, and computation capability of the modern machines we design today. As a result, many key applications' performance, efficiency and scalability are bottlenecked by data movement. In this lecture, we describe three major shortcomings of modern architectures in terms of 1) dealing with data, 2) taking advantage of the vast amounts of data, and 3) exploiting different semantic properties of application data. We argue that an intelligent architecture should be designed to handle data well. We show that handling data well requires designing architectures based on three key principles: 1) data-centric, 2) data-driven, 3) data-aware. We give several examples for how to exploit each of these principles to design a much more efficient and high performance computing system. We especially discuss recent research that aims to fundamentally reduce memory latency and energy, and practically enable computation close to data, with at least two promising novel directions: 1) processing using memory, which exploits analog operational properties of memory chips to perform massively-parallel operations in memory, with low-cost changes, 2) processing near memory, which integrates sophisticated additional processing capability in memory controllers, the logic layer of 3D-stacked memory technologies, or memory chips to enable high memory bandwidth and low memory latency to near-memory logic. We show both types of architectures can enable orders of magnitude improvements in performance and energy consumption of many important workloads, such as graph analytics, database systems, machine learning, video processing. We discuss how to enable adoption of such fundamentally more intelligent architectures, which we believe are key to efficiency, performance, and sustainability. We conclude with some guiding principles for future computing architecture and system designs.

Tutorial: Boosting Productivity and Applications Performance on Parallel Distributed Systems with the SHAD C++ Library
  • Vito Giovanni Castellana (Pacific Northwest National Laboratory, Richland, WA)
  • Marco Minutoli (Pacific Northwest National Laboratory, Richland, WA)
  • John Feo (Pacific Northwest National Laboratory, Richland, WA)

As the complexity and scale of High Performance Computing systems grows (node, core, and accelerators counts, memory, network), so does the complexity of applications, and thus, the demand for portability and productivity. With these issues in mind, we have designed SHAD, the Scalable High-performance Algorithms and Data-structures library. SHAD is open source software, written in C++, for C++ developers. Unlike other HPC libraries for distributed systems, which rely on SPMD models, SHAD adopts a shared-memory programming abstraction, to make C++ programmers feel at home. Thanks to its abstraction layers, SHAD can target different systems, ranging from laptops to HPC clusters, without any need for modifying the user-level code. In this tutorial, we first overview the design of the SHAD library, depicting its main components: runtime systems abstractions for tasking; parallel and distributed data-structures; STL-compliant interfaces and algorithms. We then propose an interactive hands-on session, with coding exercises covering the different components of the software, from the tasking API up to the STL algorithms and Data Structures layer. The SHAD library is available at https://github.com/pnnl/SHAD.

Tutorial: NVMExplorer: A Framework for Cross-Stack Comparisons of Embedded Non-Volatile Memory Solutions
  • Lilian Pentecost (Amherst College)
  • Alexander Hankin (Harvard / Intel)
  • Marco Donato (Tufts)
  • Mark Hempstead (Tufts)
  • Gu-Yeon Wei (Harvard)
  • David Brooks (Harvard)

NVMExplorer is a design space exploration framework that addresses key memory system design questions and reveals opportunities and optimizations for embedded NVMs under realistic system-level constraints, while providing a flexible interface and modular evaluation to empower further investigations. This tutorial will walk through hands-on design studies using our open-source code base, give instruction for how to use our interactive data visualization dashboard, and highlight our most recent additions to the framework, including new data-intensive workload characteristics and 3D-integrated memory solutions. We will additionally guide attendees to configure and run their own design studies according to their interests. See our webpage (http://nvmexplorer.seas.harvard.edu/) for details.

Sunday, October 9, 2022

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Time What Where
8:00–9:00 Continental Breakfast Classroom B, DPI
9:00–10:30 Workshop: NextPIM, pt. 1 Discovery Room, DPI
9:00–10:30 Tutorial: SODA Synthesizer, pt. 1 Orange & Blue Room, IC
10:30–11:00 Coffee Break Classroom B, DPI
11:00–12:30 Tutorials / Workshops Resume (locations as above)
12:30–14:00 Lunch Classroom B, DPI
14:15–15:45 Workshop: NextPIM, pt. 2 Discovery Room, DPI
14:15–15:45 Tutorial: SODA Synthesizer, pt. 2 Orange & Blue Room, IC
14:15–15:45 Tutorial: SYCL Classroom A, DPI
15:45–16:15 Coffee Break Classroom B, DPI
16:15–17:45 Tutorials / Workshops Resume (locations as above)
Time What Where

Workshop: NextPIM --- Evolution of PIM for Next-Generation Computing
  • Karthik Swaminathan (IBM Research)
  • Saransh Gupta (IBM Research)
  • Vijaykrishnan Narayanan (Penn State University)

Over the past decade or so, Processing in Memory (PIM) has evolved from single-cell demonstrations to large scale commercial deployment by leading processor and memory vendors. This workshop recognizes the pervasiveness of PIM across several application domains that form the backbone of computing systems today. It seeks to identify the successes, failures, and future opportunities for these technologies, potential new applications, and the development of tools, runtimes and other essential infrastructure that will dictate ongoing research this field.

See the workshop page for more information.

Tutorial: SODA Synthesizer: Accelerating Data Science Applications with an end-to-end Silicon Compiler
  • Nicolas Bohm Agostini
  • Serena Curzel
  • Michele Fiorito
  • Vito Giovanni Castellana
  • Fabrizio Ferrandi
  • Antonino Tumeo

Data Science applications (machine learning, graph analytics) are among the main drivers for the renewed interests in designing domain specific accelerators, both for reconfigurable devices (Field Programmable Gate Arrays) and Application-Specific Integrated Circuits (ASICs). Today, the availability of new high-level synthesis (HLS) tools to generate accelerators starting from high-level specifications provides easier access to FPGAs or ASICs and preserves programmer productivity. However, the conventional HLS flow typically starts from languages such as C, C++, or OpenCL, heavily annotated with information to guide the hardware generation, still leaving a significant gap with respect to the (Python based) data science frameworks.

This tutorial will discuss HLS to accelerate data science on FPGAs or ASICs, highlighting key methodologies, trends, advantages, benefits, but also gaps that still need to be closed. The tutorial will provide a hands-on experience of the SOftware Defined Accelerators (SODA) Synthesizer, a toolchain composed of SODA-OPT, an opensource front-end and optimizer that interface with productive programming data science frameworks in Python, and Bambu, the most advanced open-source HLS tool available, able to generate optimized accelerators for data-intensive kernels.

Tutorial: SYCL for heterogenous computing: updates, experience, and feedback
  • Zheming Jin (ORNL)

SYCL programming is based on standard ISO C++ with higher-level abstraction. It is a promising programming model for CPU, GPU, and other accelerators. The tutorial is organized as invited talks from researchers and developers in the SYCL community.  Whether or not you are familiar with SYCL, we hope that the tutorial will be interesting and valuable to your work.

Important Dates and Deadlines

Conference Papers:

  • Abstracts: April 18, 2022
  • Full Papers: April 25, 2022
  • Round 1 Rebuttal: June 6–9, 2022
  • Round 2 Rebuttal: July 11–14, 2022
  • Author Notification: July 29, 2022
  • Camera Ready Papers: August 26, 2022

Posters:

  • Poster Submission Deadline: September 1, 2022
  • Author Notification: September 15, 2022
  • Extended Abstract: September 29, 2022
  • Poster Session: October 10, 2022

ACM Student Research Competition:

  • Abstract Submission Deadline: September 8, 2022
  • Author Notification: September 16, 2022
  • SRC Poster Session: October 11, 2022
  • SRC Finalist Presentations: October 12, 2022

Student Travel Awards:

  • Application Deadline: October 5, 2022

Workshops and Tutorials:

  • Workshops/Tutorials: October 8–9, 2022

Conference: October 10–12, 2022


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Supported by Argonne National Laboratory.