[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 relationships between elements of a data set *，Without regard to what the data set actually is *。 Graph theoretical (abstract) results can be applied to many different practical (concrete) problems -theory 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

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

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 task graph What is Task Graph-based Programming System (TGPS) Code A B C D B A C D 6• TGPS encapsulates function calls and their dependencies in a top-down task graph What is Task Graph-based precede(task_d); 19 20 sched.schedule(); 21 sched.wait(); B A C D 1. Easy to write and express a task graph 2. Allow to implement irregular parallel decomposition strategies 8Existing TGPSs on Heterogenous

(Vasia) Kalavri
 vkalavri@bu.edu Spring 2020 4/28: Graph Streaming ??? Vasiliki Kalavri | Boston University 2020 Modeling the world as a graph 2 Social networks friend follows The web Actor-movie results for the search term “graph” ??? Vasiliki Kalavri | Boston University 2020 Basics 1 5 4 3 2 “node” or “vertex” “edge” 1 5 4 3 2 undirected graph directed graph 4 ??? Vasiliki Kalavri Kalavri | Boston University 2020 Graph streams Graph streams model interactions as events that update an underlying graph structure 5 Edge events: A purchase, a movie rating, a like on an online post

ingests a FP32 graph and a small dataset • Finds suitable quantization scale • Produces a quantized graph • Compiling Pre-quantized models – QNN Dialect • TVM ingests a pre-quantized graph in TFLite or rights reserved. TVM Overview Framework Graph Mxnet TF …. parsers Relay Graph Target-independent Relay passes Target-optimized graph Target-dependent Relay passes Intel x86 ARM CPU Nvidia GPU targets AutoTVM – Tuning the kernels Optimized Binary Codegen – LLVM, Cuda, C, … Framework Parsers Graph level optimizations Tensor-level optimizations Machine code generation© 2019, Amazon Web Services

from tvm import relay 2. Load a pretrained network mod, params = relay.testing.mobilenet.get_workload(batch_size=1) 3. Partition and build the network with an external codegen mod = relay.build_extern(mod build_extern(mod, “dnnl”) 4. Run the inference exe = relay.create_executor(“vm”, mod=mod, ctx=tvm.cpu(0)) data = np.random.uniform(size=(1, 3, 224, 224)).astype(“float32”) out = exe.evaluate()(data, **params) How System Overview Relay IR Graph Annotation with Your Annotator Graph Partitioning Your Codegen LLVM, CUDA, Metal, VTA Serialized Subgraph Library Relay Runtime (VM, Graph Runtime, Interpreter)

dependent: arange, nms, etc. ○ Control flow: concatenate within a while loop Limitation of TVM/graph runtime ● Cannot compile and run dynamic models© 2019, Amazon Web Services, Inc. or its Affiliates at runtime ● Virtual machine as a new runtime for Relay ● Dynamic codegen (WIP) ○ Kernel dispatch for a single op ○ Graph dispatch for a (sub-)graph In collaboration with Jared Roesch, Zhi Chen, Wei Wei Chen© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. “Any” in Relay typing Any: represent an unknown dimension at compilation time. Define a tensor type: Tensor<(Any, 3, 32

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Keyspace Graph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Replication Graph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 vtctl Replication Graph Command Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .