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▼a 1104698732
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▼z (OCoLC)1108689990
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▼a QA76.73.J85
▼b S36 2019
▼a 005.13/3
▼2 23
▼a 005.73
▼2 23
▼a Sengupta, Avik,
▼e author.
▼a Julia high performance:
▼b optimizations, distributed computing, multithreading, and GPU programming with Julia 1.0 and beyond /:
▼c Avik Sengupta.
▼a Second edition.
▼a Birmingham:
▼b Packt Publishing Ltd.,
▼c 2019.
▼a 1 online resource.
▼a text
▼b txt
▼2 rdacontent
▼a computer
▼b c
▼2 rdamedia
▼a online resource
▼b cr
▼2 rdacarrier
▼a Port sharing for high-performance web serving
▼a Includes index.
▼a Cover; Title Page; Copyright and Credits; Dedication; About Packt; Foreword; Contributors; Table of Contents; Preface; Chapter 1: Julia is Fast; Julia -- fast and dynamic; Designed for speed; JIT and LLVM; Types, type inference, and code specialization; How fast can Julia be?; Summary; Chapter 2: Analyzing Performance; Timing Julia functions; The @time macro; Other time macros; The Julia profiler; Using the profiler; ProfileView; Using Juno for profiling; Using TimerOutputs; Analyzing memory allocation; Using the memory allocation tracker; Statistically accurate benchmarking
▼a Using BenchmarkTools.jlSummary; Chapter 3: Types, Type Inference, and Stability; The Julia type system; Using types; Multiple dispatch; Abstract types; Julia's type hierarchy; Composite and immutable types; Type parameters; Type inference; Type-stability; Definitions; Fixing type instability; The performance pitfalls; Identifying type stability; Loop variables; Kernel methods and function barriers; Types in storage locations; Arrays; Composite types; Parametric composite types; Summary; Chapter 4: Making Fast Function Calls; Using globals; The trouble with globals
▼a Fixing performance issues with globalsInlining; Default inlining; Controlling inlining; Disabling inlining; Constant propagation; Using macros for performance; The Julia compilation process; Using macros; Evaluating a polynomial; Horner's method; The Horner macro; Generated functions; Using generated functions; Using generated functions for performance; Using keyword arguments; Summary; Chapter 5: Fast Numbers; Numbers in Julia, their layout, and storage; Integers; Integer overflow; BigInt; The floating point; Floating point accuracy; Unsigned integers; Trading performance for accuracy
▼a The @fastmath macroThe K-B-N summation; Subnormal numbers; Subnormal numbers to zero; Summary; Chapter 6: Using Arrays; Array internals in Julia; Array representation and storage; Column-wise storage; Adjoints; Array initialization; Bounds checking; Removing the cost of bounds checking; Configuring bound checks at startup; Allocations and in-place operations; Preallocating function output; sizehint!; Mutating functions; Broadcasting; Array views; SIMD parallelization (AVX2, AVX512); SIMD.jl; Specialized array types; Static arrays; Structs of arrays; Yeppp!
▼a Writing generic library functions with arraysSummary; Chapter 7: Accelerating Code with the GPU; Technical requirements; Getting started with GPUs; CUDA and Julia; CuArrays; Monte Carlo simulation on the GPU; Writing your own kernels; Measuring GPU performance; Performance tips; Scalar iteration; Combining kernels; Processing more data; Deep learning on the GPU; ArrayFire; Summary; Chapter 8: Concurrent Programming with Tasks; Tasks; Using tasks; The task life cycle; task_local_storage; Communicating between tasks; Task iteration; High-performance I/O
▼a Julia is a high-level, high-performance dynamic programming language for numerical computing. This book will help you understand the performance characteristics of your Julia programs and achieve near-C levels of performance in Julia.
▼a Description based on online resource; title from digital title page (viewed on August 03, 2020).
▼a Master record variable field(s) change: 050, 082
▼a Julia (Computer program language)
▼a Application software
▼x Development.
▼a Application software
▼x Development.
▼2 fast
▼0 (OCoLC)fst00811707
▼a Julia (Computer program language)
▼2 fast
▼0 (OCoLC)fst01938397
▼a Electronic books.
▼a Edelman, Alan.
▼i Print version:
▼a Sengupta, Avik.
▼t Julia High Performance : Optimizations, Distributed Computing, Multithreading, and GPU Programming with Julia 1. 0 and Beyond, 2nd Edition.
▼d Birmingham : Packt Publishing, Limited, ©2019,
▼z 9781788298117
▼3 EBSCOhost
▼u http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2158182
▼a Askews and Holts Library Services
▼b ASKH
▼n BDZ0040173887
▼a ProQuest Ebook Central
▼b EBLB
▼n EBL5788735
▼a EBSCOhost
▼b EBSC
▼n 2158182
▼a YBP Library Services
▼b YANK
▼n 300607329
▼a 강리원
▼a eBook
▼a 92
▼b N$T
| 자료유형 : | eBook |
|---|---|
| ISBN : | 9781788292306 |
| ISBN : | 1788292308 |
| ISBN : | |
| 개인저자 : | Sengupta, Avik, author. |
| 서명/저자사항 : | Julia high performance: optimizations, distributed computing, multithreading, and GPU programming with Julia 1.0 and beyond /: Avik Sengupta. |
| 판사항 : | Second edition. |
| 발행사항 : | Birmingham: Packt Publishing Ltd., 2019. |
| 형태사항 : | 1 online resource. |
| 일반주기 : | Port sharing for high-performance web serving |
| 일반주기 : | Includes index. |
| 내용주기 : | Cover; Title Page; Copyright and Credits; Dedication; About Packt; Foreword; Contributors; Table of Contents; Preface; Chapter 1: Julia is Fast; Julia -- fast and dynamic; Designed for speed; JIT and LLVM; Types, type inference, and code specialization; How fast can Julia be?; Summary; Chapter 2: Analyzing Performance; Timing Julia functions; The @time macro; Other time macros; The Julia profiler; Using the profiler; ProfileView; Using Juno for profiling; Using TimerOutputs; Analyzing memory allocation; Using the memory allocation tracker; Statistically accurate benchmarking |
| 내용주기 : | Using BenchmarkTools.jlSummary; Chapter 3: Types, Type Inference, and Stability; The Julia type system; Using types; Multiple dispatch; Abstract types; Julia's type hierarchy; Composite and immutable types; Type parameters; Type inference; Type-stability; Definitions; Fixing type instability; The performance pitfalls; Identifying type stability; Loop variables; Kernel methods and function barriers; Types in storage locations; Arrays; Composite types; Parametric composite types; Summary; Chapter 4: Making Fast Function Calls; Using globals; The trouble with globals |
| 내용주기 : | Fixing performance issues with globalsInlining; Default inlining; Controlling inlining; Disabling inlining; Constant propagation; Using macros for performance; The Julia compilation process; Using macros; Evaluating a polynomial; Horner's method; The Horner macro; Generated functions; Using generated functions; Using generated functions for performance; Using keyword arguments; Summary; Chapter 5: Fast Numbers; Numbers in Julia, their layout, and storage; Integers; Integer overflow; BigInt; The floating point; Floating point accuracy; Unsigned integers; Trading performance for accuracy |
| 내용주기 : | The @fastmath macroThe K-B-N summation; Subnormal numbers; Subnormal numbers to zero; Summary; Chapter 6: Using Arrays; Array internals in Julia; Array representation and storage; Column-wise storage; Adjoints; Array initialization; Bounds checking; Removing the cost of bounds checking; Configuring bound checks at startup; Allocations and in-place operations; Preallocating function output; sizehint!; Mutating functions; Broadcasting; Array views; SIMD parallelization (AVX2, AVX512); SIMD.jl; Specialized array types; Static arrays; Structs of arrays; Yeppp! |
| 내용주기 : | Writing generic library functions with arraysSummary; Chapter 7: Accelerating Code with the GPU; Technical requirements; Getting started with GPUs; CUDA and Julia; CuArrays; Monte Carlo simulation on the GPU; Writing your own kernels; Measuring GPU performance; Performance tips; Scalar iteration; Combining kernels; Processing more data; Deep learning on the GPU; ArrayFire; Summary; Chapter 8: Concurrent Programming with Tasks; Tasks; Using tasks; The task life cycle; task_local_storage; Communicating between tasks; Task iteration; High-performance I/O |
| 요약 : | Julia is a high-level, high-performance dynamic programming language for numerical computing. This book will help you understand the performance characteristics of your Julia programs and achieve near-C levels of performance in Julia. |
| 일반주제명 : | Julia (Computer program language) -- |
| 일반주제명 : | Application software -- Development. -- |
| 일반주제명 : | Application software -- Development. -- |
| 일반주제명 : | Julia (Computer program language) -- |
| 개인저자 : | Edelman, Alan. |
| 기타형태 저록 : | Print version: Sengupta, Avik. Julia High Performance : Optimizations, Distributed Computing, Multithreading, and GPU Programming with Julia 1. 0 and Beyond, 2nd Edition. Birmingham : Packt Publishing, Limited, ©2019, 9781788298117 |
| 언어 | 영어 |
| URL : |
|---|
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