[tile] DB libraries, and large-scale graph analytics. Open-source software projects resulting from his work include the Matrix Template Library, the Boost Graph Library and Open MPI. 。Phil Ratzloff *。Distinguished Graphs Are Fundamental Abstractions * Graphs model relationships between elements of a data set *，Without regard to what the data set actually is *。 Graph theoretical (abstract) results can be applied cvMuNKCATIOWS 有二有 灿瑟必帮 和一是/第 手太则光大 A，训四 昱 站 -本| 厅 是，主 半一司、|全之体 ~ 对 /人-看 奋才全 和 全原 和 刁曾 The Future ls Big Graph: on meetalThngs soneaiadman MD] Basic Principles *。The C++ standard library (nee STL) provides

Selection ✓ Order by ❏ Group by ✓ Query ✓ Limit ✓ Expression Compatibility ✓ Join ✓ Subquery ✓ Graph traverse ✓ Out direction ✓ In direction ✓ Bidirection ✓ Edge traverse ✓ Distinct Index 01.

Scott, Principal Engineer at CMU SEI Graph/ML/AI algorithms for large- and small- scale parallel systems. Working on GBTL, a linear algebra-based C++ library for graph analytics.[DISTRIBUTION STATEMENT GraphBLAS community, C API Overview of our draft C++ API How might this interoperate with standard C++, graph library proposal? 4[DISTRIBUTION STATEMENT A] This material has been approved for public release GraphBLAS community, C API Overview of our draft C++ API How might this interoperate with standard C++, graph library proposal? 5[DISTRIBUTION STATEMENT A] This material has been approved for public release

tuples - (Possibly some additional CPOs for ordered multi-dimensional iteration) Shout out to Graph Library Proposal P1709Distributed Dense and Sparse Matrices - Distributed matrix data structures

Knowledge Graph meet Python Yongpan Sheng 目录 CONTENTS The Pipeline of Knowledge Graph Construction by Data- driven manner Python Tools for Graph Data Management Domain-specific Knowledge Graph Construction relation, object>  Mapping from natural questions to structured queries executable on knowledge graph （机器的潜台词：“我”会推理，so easy ！）。 所以，通俗的来说，在AI system中：要么从原有的知识体系中直接提取信息来使用，要 么进行推理。 将知识融合在机器中，使机器能够利 BigKE将显著提升机器的认知水平。 Preliminaries 本页PPT借鉴于复旦大学肖仰华老师《大数据时代的知识工程与知识管理》 Knowledge Graph – KG引领KE复兴  Knowledge graph is a large-scale semantic network consisting of entities and concepts as well as

Memory Model C++11 – C++23About Me: alex.dathskovsky@speedata.io www.linkedin.com/in/alexdathskovsky https://www.cppnext.comAlex Dathskovsky | alex.dathskovsky@speedata.io | www.linkedin.com/in/a

Agenda • Understand the motivation behind Taro • Learn to use the Taro C++ programming model • Dive into the Taro’s coroutine-aware scheduling algorithm • Evaluate Taro on microbenchmarks and a real-world Conclusion 2Agenda • Understand the motivation behind Taro • Learn to use the Taro C++ programming model • Dive into the Taro’s coroutine-aware scheduling algorithm • Evaluate Taro on microbenchmarks and top-down task graph What is Task Graph-based Programming System (TGPS) Code 4• TGPS encapsulates function calls and their dependencies in a top-down task graph What is Task Graph-based Programming

tf.float16) 低精度浮点数的优点 1.节约内存/显存的使用（FP16为原来的1/2，int8为原来的1/4） 2.特殊的硬件专门用于低精度浮点数的计算加速（TensorCore） Model Speedup BERT Q&A 3.3X speedup GNMT 1.7X speedup NCF 2.6X speedup ResNet-50-v1.5 3.3X speedup tf.train.AdamOptimizer() # Modify optimizer in graph, copy between fp32 weight and fp16 weight opt = tf.train.experimental.enable_mixed_precision_graph_rewrite(opt) train_op = opt.miminize(loss) AMP的白名单/灰名单/黑名单 input_saved_model_dir=input_saved_model_dir) converter.convert() converter.save(output_saved_model_dir) with tf.Session() as sess: # First load the SavedModel into the session tf.saved_model.loader.load(

guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2 New graph model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2 files examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242 7.6 Graph examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 632 8.8 The binary model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 634 8.9

guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2 New graph model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2 files examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218 7.6 Graph examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 597 8.8 The binary model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 598 8.9