11040804.pdf Source: https://www.slideshare.net/brendangregg/kernel-recipes-2017-performance-analysis-with-bpf Workflow  o Source: ebpfbasics-190611051559.pdf 2.2 Source: ebpfbasics-190611051559.pdf Source: https://kernel-recipes.org/en/2017/talks/performance-analysis-with-bpf/ bpf() system call  http://www.man7.org/linux/man-pages/man2/bpf.2.html Source: https://ossna2017.sched.com/event/BCsg/making-the-kernels-networking-data- path-programmable-with-bpf-and-xdp-daniel-borkmann-covalent GCC (GCC support for

............................................................................ 15 OOP IN PYTHON – DATA STRUCTURES ...................................................................................... ............................................................ 34 Working with Class and Instance Data ................................................................................................. techniques used for modelling complex systems by describing a collection of interacting objects via their data and behavior. Python, an Object Oriented programming (OOP), is a way of programming that focuses

of legacy code There are tools for automatically adding draft annotations based on simple static analysis or on type profiles collected at runtime. Tools include MonkeyType, autotyping and PyAnnotate. batch_proc(data: Iterable[bytes], cb_results: Combiner) -> bytes: (continues on next page) 50 Chapter 1. Contents Mypy Documentation, Release 1.8.0 (continued from previous page) for item in data: ... a Note: The same will work with isinstance(x := a, float) as well. 1.11.4 Limitations Mypy’s analysis is limited to individual symbols and it will not track relationships between symbols. For example

is imported from typing or typing_extensions [unimported-reveal] Additional features Dataclasses Data Class Transforms The attrs package Using a remote cache to speed up mypy runs Extended Callable types of legacy code There are tools for automatically adding draft annotations based on simple static analysis or on type profiles collected at runtime. Tools include MonkeyType [https://monkeytype.readthedocs maxlen: Optional[int] = None) -> list[bytes]: ... def batch_proc(data: Iterable[bytes], cb_results: Combiner) -> bytes: for item in data: ... def good_cb(*vals: bytes, maxlen: Optional[int] =

is imported from typing or typing_extensions [unimported-reveal] Additional features Dataclasses Data Class Transforms The attrs package Using a remote cache to speed up mypy runs Extended Callable types of legacy code There are tools for automatically adding draft annotations based on simple static analysis or on type profiles collected at runtime. Tools include MonkeyType [https://monkeytype.readthedocs maxlen: Optional[int] = None) -> list[bytes]: ... def batch_proc(data: Iterable[bytes], cb_results: Combiner) -> bytes: for item in data: ... def good_cb(*vals: bytes, maxlen: Optional[int] =

of legacy code There are tools for automatically adding draft annotations based on simple static analysis or on type profiles collected at runtime. Tools include MonkeyType, autotyping and PyAnnotate. maxlen: Optional[int] = None) -> list[bytes]: ... def batch_proc(data: Iterable[bytes], cb_results: Combiner) -> bytes: for item in data: ... def good_cb(*vals: bytes, maxlen: Optional[int] = None) -> a Note: The same will work with isinstance(x := a, float) as well. 1.11.4 Limitations Mypy’s analysis is limited to individual symbols and it will not track relationships between symbols. For example

should be specialised？ Overhead Analysis For CPython Sources of performance overhead in CPython? • Mohamed Ismail and G Edward Suh. 2018. Quantitative overhead analysis for Python. In 2018 IEEE International Quantifying the interpretation overhead of Python. Science of Computer Programming 215 (2022). Overhead Analysis For CPython – opcodes execution Descriptions Opcodes name access Access the values corresponding BINARY_ADD、COMPARE_OP miscellany Others POP_TOP、END_FINALLY Overhead Analysis For CPython – language features Overhead Analysis For CPython – interpreter operations • Attributes(dictionary) caching

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 Preliminaries Preliminaries 展 Preliminaries Preliminaries 大数据时代的机遇 – 大规模知识自动获取 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》  Big Data + Machine Learning[R1] + Powerful Computation[R2] • 完全意义上的自下而上的方式 • 从海量的数据中去挖掘异构、动态、碎片化的知识 e.g., knowledge • A robot brain needs knowledge base to understand the word Preliminaries 本页PPT借鉴于复旦大学肖仰华老师 《Data-driven Approaches for Large-scale Knowledge Graph Construction》 Knowledge Graph – KG引领KE复兴  Common

multiple pyarmor in same machine How to get license information of one obfuscated package How to protect data files Examples Obfuscating and Packing PyQt Application Running obfuscated Django site with Apache script: pyarmor licenses --expired 2019-01-01 product-001 PyArmor generates new license file: Read data from .pyarmor_capsule.zip in the HOME folder Create license.lic in the licenses/product-001 folder injecting your private checkpoints in the obfuscated scripts 7. If data files need to be protected, refer to How to protect data files About the security of obfuscated scripts, refer to The Security

multiple pyarmor in same machine How to get license information of one obfuscated package How to protect data files Examples Obfuscating and Packing PyQt Application Running obfuscated Django site with Apache script: pyarmor licenses --expired 2019-01-01 product-001 PyArmor generates new license file: Read data from .pyarmor_capsule.zip in the HOME folder Create license.lic in the licenses/product-001 folder injecting your private checkpoints in the obfuscated scripts 7. If data files need to be protected, refer to How to protect data files About the security of obfuscated scripts, refer to The Security