https://graalworkshop.github.io/2021/

https://bit.ly/imop-cgo21

https://sites.google.com/view/cgo2021-dse/home

https://graalworkshop.github.io/2021/

https://llvm.org/devmtg/2021-02-28/

https://www.c4ml.org/

https://www.c4ml.org/

Atomicity without Trust

There is increasing interest in distributed systems where participants stand to benefit from cooperation but do trust one another not to cheat. Although blockchain-based commerce is perhaps the most visible example of such systems, the problem of economic exchange among mutually untrusting autonomous parties is a fundamental one independent of particular technologies.

This talk argues that such systems require rethinking our notions of correctness for distributed concurrency control and fault-tolerance. Addressing this challenge brings up questions familiar from classical distributed systems: how to combine multiple steps into a single atomic action, how to recover from failures, and how to coordinate concurrent access to data. Commerce among untrusting parties is a kind of fun-house mirror of classical distributed computing: familiar features are recognizable but distorted. For example, classical atomic transactions are often described in terms of the well-known ACID properties: atomicity, consistency, isolation, and durability. We will see that untrusting cooperation requires structures superficially similar to, but fundamentally different from, classical atomic transactions.

Speaker: Maurice Herlihy (Brown University)

Data Layout and Data Representation Optimizations to Reduce Data Movement

Code generation and optimization for the diversity of current and future architectures must focus on reducing data movement to achieve high performance. How data is laid out in memory, and representations that compress data (e.g., reduced floating point precision) have a profound impact on data movement. Moreover, the cost of data movement in a program is architecture-specific, and consequently, optimizing data layout and data representation must be performed by a compiler once the target architecture is known. With this context in mind, this talk will provide examples of data layout and data representation optimizations, and call for integrating these data properties into code generation and optimization systems.

Speaker: Mary Hall (University of Utah)

Mary Hall is a Professor and Director of the School of Computing at University of Utah. She received a PhD in Computer Science from Rice University. Her research focus brings together compiler optimizations targeting current and future high-performance architectures on real-world applications. Hall’s prior work has developed compiler techniques for exploiting parallelism and locality on a diversity of architectures: automatic parallelization for SMPs, superword-level parallelism for multimedia extensions, processing-in-memory architectures, FPGAs and more recently many-core CPUs and GPUs. Professor Hall is an IEEE Fellow, an ACM Distinguished Scientist and a member of the Computing Research Association Board of Directors. She actively participates in mentoring and outreach programs to encourage the participation of women and other groups underrepresented in computer science.

A Journey to a Commercial-Grade Processing-In-Memory (PIM) Chip Development

Emerging applications demand high off-chip memory bandwidth, but it becomes very expensive to further increase the bandwidth of off-chip memory under stringent physical constraints of chip packages and system boards. Besides, energy efficiency of moving data across the memory hierarchy of processors has steadily worsened with the stagnant technology scaling and poor data reuse characteristics of the emerging applications. To cost-effectively increase the bandwidth and energy efficiency, researchers began to reconsider the past processing-in-memory (PIM) architectures and advance them further, especially with recent integration technologies such as 2.5D/3D stacking. Albeit the recent advances, no major memory manufacturer had developed even a proof-of-concept silicon yet, not to mention a product. In this talk, I will start with discussing various practical and technical challenges that have been overlooked by researchers and prevented the industry from successfully commercializing PIM. Then I will present a practical PIM architecture that considers various aspects of successful commercialization in the near future. Finally, I present a journey to the development of a commercial-grade PIM chip, which was designed based on a commercial HBM2, fabricated with a 20nm DRAM technology, integrated with unmodified commercial processors, and successfully ran various memory-bound machine learning applications with more than 2x improvement in system performance 70% reduction in system energy consumption.

Speaker: Nam Sung Kim (University of Illinois at Urbana-Champaign / Samsung Electronics)

Nam Sung Kim is a Senior Vice President at Samsung Electronics as well as a Professor at the University of Illinois. At Samsung he led the architecture definitions and designs of next generation DRAM devices including HBM, LPDDR, DDR, and GDDR. He has published more than 200 refereed articles to highly-selective conferences and journals in the field of circuit, architecture, and computer-aided design. For his contributions to developing power-efficient computer architectures, he was elevated to IEEE and ACM Fellows in 2016 and 2021, respectively, and received the ACM SIGARCH/IEEE-CS TCCA Influential ISCA Paper Award in 2017. He is also a hall of fame member of all three major computer architecture conferences, ISCA, MICRO, and HPCA.

Best Paper Award

Compiler Graph Applications for GPUs with GraphIt

Ajay Brahmakshatriya, Yunming Zhang, Changwan Hong, Shoaib Kamil, Julian Shun, Saman Amarasinghe

Test-of-Time Award

Level by Level: Making Flow- and Context-Sensitive Pointer Analysis Scalable for Millions of Lines of Code (CGO ’10)

Hongtao Yu, Jingling Xue, Wei Huo, Xiaobing Feng, Zhaoqing Zhang

Panelists: John L. Hennessy Alphabet and Stanford, David Patterson Google and U.C. Berkeley, Margaret Martonosi NSF CISE and Princeton, Bill Dally NVIDIA and Stanford, Natalie Enright Jerger U. Toronto and ACM D&I Council, Kim Hazelwood Facebook AI Research, Timothy M. Pinkston USC