Zhihao Jia is an incoming Assistant Professor of Computer Science at CMU (starting Fall 2021). He obtained his Ph.D. at Stanford working with Alex Aiken and Matei Zaharia. His research interests lie in the intersection of computer systems and machine learning, with a focus on building efficient, scalable, and high-performance systems for ML computations. 

演讲题目：Automated Discovery of Machine Learning Optimizations 

摘要：As an increasingly important workload, machine learning (ML) applications require different performance optimization techniques from traditional runtimes and compilers. In particular, to accelerate ML applications, it is generally necessary to perform ML computations on heterogeneous hardware and parallelize computations using multiple data dimensions, neither of which is even expressible in traditional compilers and runtimes. In this talk, I will describe my work on automated discovery of performance optimizations to accelerate ML computations. TASO, the Tensor Algebra SuperOptimizer, optimizes the computation graphs of deep neural networks (DNNs) by automatically generating potential graph optimizations and formally verifying their correctness. TASO outperforms rule-based graph optimizers in existing ML systems (e.g., TensorFlow, TensorRT, and TVM) by up to 3X by automatically discovering novel graph optimizations, while also requiring significantly less human effort. FlexFlow is a system for accelerating distributed DNN training. FlexFlow identifies parallelization dimensions not considered in existing ML systems (e.g., TensorFlow and PyTorch) and automatically discovers fast parallelization strategies for a specific parallel machine. Companies and national labs are using FlexFlow to train production ML models that do not scale well in current ML systems, achieving over 10x performance improvement. I will also outline future research directions for further automating ML systems, such as codesigning ML models, software systems, and hardware backends for end-to-end ML deployment.  

博士，中国科学院计算技术研究所研究员，博士生导师。研究方向为异构芯片的编译和编程，近年来围绕大数据、AI等新型计算范式，研究这些应用在异构架构下的编译优化、编程环境优化。崔慧敏作为负责人承担自然科学基金、重点研发计划等多项国家级项目和课题，先后在PLDI、MICRO、PPoPP、TPDS等国际会议和期刊上发表论文二十余篇。

演讲题目：高性能智能处理器编程语言与编译器设计

摘要：以寒武纪平台为代表的高性能智能处理器提供了一个通用的深度学习平台，其目标是为当前和未来的智 能应用提供强大的计算能力。由于未来应用的多样性和不可预测性，提供基础的高级编程语言是其生态 构建和推广中不可缺少的一个环节。我们针对这一需求，以C语言为基础，面向应用和平台设计了通用 的高级编程语言Bang语言，解决了用户自定义算子的灵活开发问题。并进一步，利用深度的编译优化技 术来充分发挥芯片的处理能力。 

Wei Lin is currently Senior Director of Platform of Artificial Intelligence (PAI) and Chief Architect of Big-data computation platform in Alibaba. 15+ years’ experience specializing in backend/infrastructure, distributed system development, storage and a large-scale computation system include batch, streaming and machine learning. 

演讲题目：AI Compiler at Alibaba 

摘要：With the emerging AI workloads and diversity of executing computing hardware, AI compiler plays a vital role to bridge the gap between model expressive flexibility and underlying high- performance system implementation. In this talk, we will share our experiences of applying AI compiler into Alibaba's production environment, including: 1.Large-scale deployment of our AI Compiler into PAI (Platform of Artificial Intelligence) production clusters running stably for more than 6 months with tens of thousands of GPU hour saving. We will talk about our aggressive fusion and co-design strategy in which a cost-based approach is exploited to find the optimal fusion plan to boost hardware efficiency. In addition, lots of experiences to ensure that our compiler can be enabled by default in a large-scale production cluster will be shared. 2.Automatic code generation framework named as Ansor. This work has already been accepted by OSDI 2020 and deployed into our production environment. Compared with existing search strategies, Ansor explores much more optimization combinations and thus can find high-performance programs that are outside the search space of existing state-of-the-art approaches. Our evaluation shows that Ansor improves the execution performance of deep neural networks on the Intel CPU, ARM CPU, and NVIDIA GPU by up to 3:8x, 2:6x, and 1:7x, respectively. 3.Our thoughts about the future direction of AI compiler from industry perspective, such as the inter-play between compiler, runtime, resource scheduling and distributed execution. Also, we would like to raise some questions looking forward to the potential interaction between academia and industry.

Shin-Ming Liu is the Chief Architect@Xcalibyte.com. Shin-Ming started as compiler developer since early ’80. He has participated in compilation systems from scratch in various companies in Silicon Valley and established wide influence in modern day compilation systems including gcc and llvm design. Besides the in-depth compiler development work, Shin-Ming has been the Director for Java C/C++ ToolChain Lab. of HP-UX Server, Director for HP Kernel Development Lab for HP 3PAR Storage System, and developed extensive insight about computer ecosystem for high performance computing and software development productivity. 

演讲题目：Matrix multiply: from 1 to 62806 X speedup Bridging the gap between productivity and performance 

摘要：John Hennessy in his Stanford lecture discussed a new era of computing with the challenge to improve GEMM by ~63,000 times. We will further elaborate his vision with a deep dive into the compilation and runtime techniques needed. We also suggest a possible roadmap to bring productivity and performance into his vision . We argue the need for an open source platform that enables multiple languages co-exist in the compilation and runtime while allow individual chip/accelerator vendors to specialize for their target domain. We will analyze the technical challenges ahead and possible directions moving forward for a thriving industry in AI and data science.