cores cpu的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列問答集和資訊懶人包

The Art of Writing Efficient Programs: An advanced programmer’’s guide to efficient hardware utilization and compiler optimizati

為了解決cores cpu的問題，作者Pikus, Fedor G. 這樣論述：

Get to grips with various performance improvement techniques such as concurrency, lock-free programming, atomic operations, parallelism, and memory managementKey Features: Understand the limitations of modern CPUs and their performance impactFind out how you can avoid writing inefficient code and

get the best optimizations from the compilerLearn the tradeoffs and costs of writing high-performance programsBook Description: The great free lunch of ＂performance taking care of itself＂ is over. Until recently, programs got faster by themselves as CPUs were upgraded, but that doesn’t happen anymo

re. The clock frequency of new processors has almost peaked. New architectures provide small improvements to existing programs, but this only helps slightly. Processors do get larger and more powerful, but most of this new power is consumed by the increased number of processing cores and other ＂extr

a＂ computing units. To write efficient software, you now have to know how to program by making good use of the available computing resources, and this book will teach you how to do that.The book covers all the major aspects of writing efficient programs, such as using CPU resources and memory effici

ently, avoiding unnecessary computations, measuring performance, and how to put concurrency and multithreading to good use. You’ll also learn about compiler optimizations and how to use the programming language (C]+) more efficiently. Finally, you’ll understand how design decisions impact performanc

e.By the end of this book, you’ll not only have enough knowledge of processors and compilers to write efficient programs, but you’ll also be able to understand which techniques to use and what to measure while improving performance. At its core, this book is about learning how to learn.What You Will

Learn: Discover how to use the hardware computing resources in your programs effectivelyUnderstand the relationship between memory order and memory barriersFamiliarize yourself with the performance implications of different data structures and organizationsAssess the performance impact of concurren

t memory accessed and how to minimize itDiscover when to use and when not to use lock-free programming techniquesExplore different ways to improve the effectiveness of compiler optimizationsDesign APIs for concurrent data structures and high-performance data structures to avoid inefficienciesWho thi

s book is for: This book is for experienced developers and programmers who work on performance-critical projects and want to learn different techniques to improve the performance of their code. Programmers who belong to algorithmic trading, gaming, bioinformatics, computational genomics, or computat

ional fluid dynamics communities can learn various techniques from this book and apply them in their domain of work.Although this book uses the C++ language, the concepts demonstrated in the book can be easily transferred or applied to other compiled languages such as C, Java, Rust, Go, and more.

cores cpu進入發燒排行的影片

基於運行環境事件之程式行為萃取及其應用

為了解決cores cpu的問題，作者李泓暐 這樣論述：

由於電腦科技逐漸在各處都可見其身影和日益複雜的電腦系統，了解那些在電腦系統中的程式行為無可避免地成為一個重要的議題。鑒於在沒有程式輸入下僅靠程式原始碼來分析程式是有困難的，又或者靠程式機器碼，但即使是一個簡單程式，其機器碼也相當複雜而難以分析，本篇論文轉而藉由觀測那些在程序(運行中的程式)所觸發的事件來分析程式行為。雖然那些事件自然地蘊含了許多關於該程式的資訊，但相對的，種類繁多的事件語意和格式仰賴於執行環境，若要有較好的偵測效率或效果，將使得觀測或分析時期的優化需要大量的專家知識。除此之外，一次大量的事件或是快速的事件生成速率(尤其是在觀測低階事件)皆會限制其線上應用。本篇論文將提出兩個程

式行為模型涵蓋了三種不同面向的程式行為，這些行為在現今環境都有非常廣泛的應用。流域模型(Watershed model)針對程式計算量和並行性，而輪廓模型(Profile model)針對程式功能性，而且這些模型的輸入能夠非常簡單地從真實系統事件中進行對應，不需要大量專家知識以及完整的事件語意。為了證明這些模型的實務價值，本篇論文實作了兩個雛型系統分別基於其中一個模型在效能分析和安全應用上，並且進行充分地評估。

Fpgas for Software Programmers

為了解決cores cpu的問題，作者Koch, Dirk (EDT)/ Hannig, Frank (EDT)/ Ziener, Daniel (EDT) 這樣論述：

This book makes powerful Field Programmable Gate Array (FPGA) and reconfigurable technology accessible to software engineers by covering different state-of-the-art high-level synthesis approaches (e.g., OpenCL and several C-to-gates compilers). It introduces FPGA technology, its programming model, a

nd how various applications can be implemented on FPGAs without going through low-level hardware design phases. Readers will get a realistic sense for problems that are suited for FPGAs and how to implement them from a software designer's point of view. The authors demonstrate that FPGAs and their p

rogramming model reflect the needs of stream processing problems much better than traditional CPU or GPU architectures, making them well-suited for a wide variety of systems, from embedded systems performing sensor processing to large setups for Big Data number crunching. This book serves as an inva

luable tool for software designers and FPGA design engineers who are interested in high design productivity through behavioural synthesis, domain-specific compilation, and FPGA overlays.Introduces FPGA technology to software developers by giving an overview of FPGA programming models and design tool

s, as well as various application examples;Provides a holistic analysis of the topic and enables developers to tackle the architectural needs for Big Data processing with FPGAs;Explains the reasons for the energy efficiency and performance benefits of FPGA processing;Provides a user-oriented approac

h and a sense for where and how to apply FPGA technology. Dirk Koch is a lecturer in the Advanced Processor Technologies Group at the University of Manchester. His main research interest is on run-time reconfigurable systems based on FPGAs, including methods, tools and applications. Current resear

ch projects include database acceleration using FPGAs based on stream processing as well as reconfigurable instruction set extensions for CPUs. Dirk was a program co-chair of the FPL2012 conference and he is a program committee member of many FPGA related conferences and workshops. He is author of t

he book ＂Partial Reconfiguration on FPGAs,＂ he holds two patents, and he has (co-)authored over 50 conference and journal publications. Frank Hannig leads the Architecture and Compiler Design Group in the CS Department at the Friedrich-Alexander University Erlangen-Nürnberg (FAU), Germany, since 200

4. He received a diploma degree in an interdisciplinary course of study in EE and CS from the University of Paderborn, Germany in 2000 and a Ph.D. degree (Dr.-Ing.) in CS from FAU in 2009. His main research interests are the design of massively parallel architectures, ranging from dedicated hardware

to multi-core architectures, mapping methodologies for domain-specific computing, and architecture/compiler co-design. Frank has authored or co-authored more than 120 peer-reviewed publications. He serves on the program committees of several international conferences (ARC, ASAP, CODES+ISSS, DATE, D

ASIP, SAC). Frank is a senior member of the IEEE and an affiliate member of the European Network of Excellence on High Performance and Embedded Architecture and Compilation (HiPEAC).Daniel Ziener is currently a substitute professor for Cyber-Physical Systems at the Technische Universität Hamburg-Har

burg, Germany. From 2010 to 2015, he had led the Reconfigurable Computing Group in the Computer Science Department at Friedrich-Alexander University Erlangen-Nürnberg (FAU), Germany. His main research interests are the usage of partial dynamic reconfiguration of FPGAs, efficient usage of FPGA struct

ures, design of signal processing FPGA cores, reliable and fault tolerant embedded systems, as well as security in FPGA-based systems. Daniel has (co-)authored more than 35 peer-reviewed publications, holds two patents, and serves as a program committee member of several international conferences (D

ATE, FPL, Reconfig, SPL) as well as a reviewer for several international journals.

分散式FM-index基因搜尋於基於RISC-V的近DRAM處理之設計研究

為了解決cores cpu的問題，作者劉沛宜 這樣論述：

FM-index是一個能很有效精準比對基因序列的資料結構，並且被廣用在各種基因分析的應用上。FM-index資料結構應用在基因分析上很節省空間並且有很低的計算複雜度。然而，因為其資料存取的隨機性和密集度，再加上現今電腦架構CPU和記憶體的速度差距，使得FM-index比對基因序列的計算主要卡在記憶體的存取。近DRAM處理(NDP)是解決記憶體存取瓶頸的趨勢。我們在這篇研究提出兩種分散式FM-index基因搜尋，包含完整的資料劃分、計算分散和中央管理方法，以將計算分散到整個平行計算NDP架構上。另外，在我們NDP架構中，我們使用多個RISC-V 運算核心搭配coprocessors作為處理單元

以提供切換計算和參數的彈性和針對FM-index重複的運算加速。與直接在CPU上用軟體計算相比，我們提出的兩種FM-index基因搜尋分散方法在我們的平行NDP系統上分別達到了2.66倍和6.39倍的加速。此外，我們有完整的比較了兩種分散式方法的效能表現不同以及各自最佳的使用場景，也呈現兩種不同硬體複雜度coprocessor設計的速度表現和影響。