MARC 닫기

• LDR

  05728cam a2200637Ki 4500

• 001

  000001333168

• 005

  20210114162117

• 006

  m d

• 007

  cr cnu---unuuu

• 008

  190615s2019 enk o 001 0 eng d

• 015

  ▼a  GBB9B4127
  ▼2  bnb

• 016

  ▼a  019446072
  ▼2  Uk

• 019

  ▼a  1104698732
  ▼a  1108689990

• 020

  ▼a  9781788292306
  ▼q  electronic book

• 020

  ▼a  1788292308
  ▼q  electronic book

• 020

  ▼z  9781788298117
  ▼q  paperback

• 035

  ▼a  2158182
  ▼b  (N$T)

• 035

  ▼a  (OCoLC)1104712904
  ▼z  (OCoLC)1104698732
  ▼z  (OCoLC)1108689990

• 037

  ▼a  1FB931C7-50F0-4997-A451-237F00B38033
  ▼b  OverDrive, Inc.
  ▼n  http://www.overdrive.com

• 040

  ▼a  EBLCP
  ▼b  eng
  ▼e  rda
  ▼e  pn
  ▼c  EBLCP
  ▼d  TEFOD
  ▼d  UKMGB
  ▼d  OCLCF
  ▼d  OCLCQ
  ▼d  YDX
  ▼d  OCLCQ
  ▼d  UKAHL
  ▼d  OCLCQ
  ▼d  N$T
  ▼d  OCLCQ
  ▼d  YDXIT
  ▼d  248023

• 050

  ▼a  QA76.73.J85
  ▼b  S36 2019

• 082

  ▼a  005.13/3
  ▼2  23

• 082

  ▼a  005.73
  ▼2  23

• 100

  ▼a  Sengupta, Avik,
  ▼e  author.

• 245

  ▼a  Julia high performance:
  ▼b  optimizations, distributed computing, multithreading, and GPU programming with Julia 1.0 and beyond /:
  ▼c  Avik Sengupta.

• 250

  ▼a  Second edition.

• 260

  ▼a  Birmingham:
  ▼b  Packt Publishing Ltd.,
  ▼c  2019.

• 300

  ▼a  1 online resource.

• 336

  ▼a  text
  ▼b  txt
  ▼2  rdacontent

• 337

  ▼a  computer
  ▼b  c
  ▼2  rdamedia

• 338

  ▼a  online resource
  ▼b  cr
  ▼2  rdacarrier

• 500

  ▼a  Port sharing for high-performance web serving

• 500

  ▼a  Includes index.

• 505

  ▼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

• 505

  ▼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

• 505

  ▼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

• 505

  ▼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!

• 505

  ▼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

• 520

  ▼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.

• 588

  ▼a  Description based on online resource; title from digital title page (viewed on August 03, 2020).

• 590

  ▼a  Master record variable field(s) change: 050, 082

• 650

  ▼a  Julia (Computer program language)

• 650

  ▼a  Application software
  ▼x  Development.

• 650

  ▼a  Application software
  ▼x  Development.
  ▼2  fast
  ▼0  (OCoLC)fst00811707

• 650

  ▼a  Julia (Computer program language)
  ▼2  fast
  ▼0  (OCoLC)fst01938397

• 655

  ▼a  Electronic books.

• 700

  ▼a  Edelman, Alan.

• 776

  ▼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

• 856

  ▼3  EBSCOhost
  ▼u  http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2158182

• 938

  ▼a  Askews and Holts Library Services
  ▼b  ASKH
  ▼n  BDZ0040173887

• 938

  ▼a  ProQuest Ebook Central
  ▼b  EBLB
  ▼n  EBL5788735

• 938

  ▼a  EBSCOhost
  ▼b  EBSC
  ▼n  2158182

• 938

  ▼a  YBP Library Services
  ▼b  YANK
  ▼n  300607329

• 990

  ▼a  강리원

• 991

  ▼a  eBook

• 994

  ▼a  92
  ▼b  N$T