## +24 ## Boosting Software Efficiency: A Case Study of 100% Performance Improvement in an Embedded C++ System ## GILI KAMMA ## 20 24 September 15 - 20 ☐ The talk today is about software development

## HUAWEI CLOUD Microservice Tool Improves Development Efficiency Department: Application Platform Service Author: Wang Qijun Date: 2019-09-20 ## Contents 1. Tool for Splitting Monolithic Applications ss-level| |Overall availability|Low|High| |Continuous evolution|Difficult|Easy| |Communication efficiency|Low|High| |Technology stack selection|Restricted|Flexible| |Scalable|Restricted|Flexible| |Reusability|Low|High| increases. ## Tool for Splitting Monolithic Applications into Microservices Improves Development Efficiency  ✓ Distributed

## +24 ## Balancing Efficiency and Flexibility: Cost of Abstractions in Embedded Systems MARCELL JUHASZ  zühlke ## whoami

rapid growth. We will establish our motivation behind seeking efficiency in deep learning models. We will also introduce core areas of efficiency techniques (compression techniques, learning techniques, automation Our hope is that even if you just read this chapter, you would be able to appreciate why we need efficiency in deep learning models today, how to think about it in terms of metrics that you care about, and models is rate-limited by their efficiency. While efficiency can be an overloaded term, let us investigate two primary aspects: ## Training Efficiency Training Efficiency involves benchmarking the model

strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. It comprises 236B total parameters, of which 21B are activated for each token, and supports a context architectures including Multi-head Latent Attention (MLA) and DeepSeekMoE. MLA guarantees efficient inference through significantly compressing the Key-Value (KV) cache into a latent vector, while DeepSeekMoE costs and inference efficiency of DeepSeek 67B (Dense) and DeepSeek-V2. ## Contents 1 Introduction 4 2 Architecture 6 2.1 Multi-Head Latent Attention: Boosting Inference Efficiency 6 2.1.1 Preliminaries:

backtrace 451 35 Performance Tips 454 35.1 Table of contents 454 35.2 General advice 455 35.3 Type inference 459 35.4 Memory management and arrays 474 35.5 Execution latency, package loading and package precompiling Languages Julia features optional typing, multiple dispatch, and good performance, achieved using type inference and just-in-time (JIT) compilation (and optional ahead-of-time compilation), implemented using LLVM run-time type inference (augmented by optional type annotations), and partly because of a strong focus on performance from the inception of the project, Julia’s computational efficiency exceeds that of

Issues 448 36 Performance Tips 450 36.1 Table of contents 450 36.2 General advice 451 36.3 Type inference 455 36.4 Memory management and arrays 470 36.5 Execution latency, package loading and package precompiling multi-threading locks 1776 107.20 Arrays with custom indices 1780 107.21 Module loading 1783 107.22 Inference 1784 107.23 Julia SSA-form IR 1786 107.24 EscapeAnalysis 1791 107.25 Ahead of Time Compilation 1806 Languages Julia features optional typing, multiple dispatch, and good performance, achieved using type inference and just-in-time (JIT) compilation (and optional ahead-of-time compilation), implemented using LLVM

Julia Execution ..... 1238 Parsing ..... 1239 Macro Expansion ..... 1239 Type Inference ..... 1239 JIT Code Generation ..... 1240 System Image ..... 1241 106.6 Calling Conventions 1245 Constructors ..... 1245 Builtins ..... 1246 Keyword arguments ..... 1246 Compiler efficiency issues ..... 1247 106.9Base.Cartesian ..... 1248 Principles of usage ..... 1248 Basic syntax 1274 106.19Module loading ..... 1275 Experimental features ..... 1275 106.20Inference ..... 1276 How inference works ..... 1276 Debugging compiler.jl ..... 1276 The inlining algorithm (inline_worthy)

1473 101.18 Arrays with custom indices ..... 1476 101.19 Module loading ..... 1480 101.20 Inference ..... 1480 101.21 Julia SSA-form IR ..... 1482 101.22 EscapeAnalysis ..... 1486 101.23 Static Julia features optional typing, multiple dispatch, and good performance, achieved using type inference and just-in-time (JIT) compilation, implemented using LLVM. It is multi-paradigm, combining features run-time type inference (augmented by optional type annotations), and partly because of a strong focus on performance from the inception of the project, Julia's computational efficiency exceeds that

multi-threading locks 1545 101.19 Arrays with custom indices 1549 101.20 Module loading 1552 101.21 Inference 1553 101.22 Julia SSA-form IR 1555 101.23 EscapeAnalysis 1558 101.24 Static analyzer annotations Julia features optional typing, multiple dispatch, and good performance, achieved using type inference and just-in-time (JIT) compilation (and optional ahead-of-time compilation), implemented using LLVM run-time type inference (augmented by optional type annotations), and partly because of a strong focus on performance from the inception of the project, Julia's computational efficiency exceeds that