Accelerating MATLAB performance : 1001 tips to speed up MATLAB programs /: 1001 tips to speed up MATLAB programs. (2015)

• Record Type: Book
• Further Information: Note: Yair M. Altman.
• Authors: Altman, Yair M
• Contents: 1. Introduction to Performance Tuning; 1.1 Why Should We Bother?; 1.2 When to Performance-Tune and When Not to Bother; 1.3 The Iterative Performance Tuning Cycle; 1.3.1 Pareto’s Principle and the Law of Diminishing Returns; 1.3.2 When to Stop Tuning; 1.3.3 Periodic Performance Maintenance; 1.4What to Tune; 1.5Performance Tuning Pitfalls; 1.5.1 When to Tune; 1.5.2 Performance Goals; 1.5.3 Profiling; 1.5.4 Optimization; 1.6 Performance Tuning Tradeoffs; 1.7 Vertical versus Horizontal Scaling; 1.8 Perceived versus Actual Performance; 1.8.1 Presenting Continuous Feedback for Ongoing Tasks; 1.8.2 Placing the User in Control; 1.8.3 Enabling User Interaction during Background Processing; 1.8.4 Streaming Data as it Becomes Available; 1.8.5 Streamlining the Application; 1.8.6 Reducing the Run-Time Variability; 1.8.7 Performance and Real Time; ; 2. Profiling MATLAB® Performance ; 2.1 The MATLAB Profiler; 2.1.1 The Detailed Profiling Report; 2.1.2 A Sample Profiling Session; 2.1.3 Programmatic Access to Profiling Data; 2.1.4 Function-Call History Timeline; 2.1.5 CPU versus Wall-Clock Profiling; 2.1.6 Profiling Techniques; 2.1.6.1 Relative versus Absolute Run Times; 2.1.6.2 Ensuring Profiling Consistency; 2.1.6.3 Ensuring Compatibility with Real-World Conditions; 2.1.6.4 Profiling GUI and I/O; 2.1.6.5 Code Coverage; 2.1.7 Profiling Limitations; 2.1.8 Profiling and MATLAB’s JIT; 2.2 tic, toc and Relatives; 2.2.1 The Built-In tic, toc Functions; 2.2.2 Comparison between the Profiler and tic, toc; 2.2.3 Related Tools; 2.3 Timed Log Files and Printouts; 2.4 Non-MATLAB Tools; ; 3. Standard Performance-Tuning Techniques ; 3.1 Loop Optimization; 3.1.1 Move Loop-Invariant Code Out of the Loop; 3.1.1.1 A Simple Example; 3.1.1.2 I/O and Memory-Related Invariants; 3.1.1.3 Subexpression Hoisting; 3.1.1.4 Loop Conditionals; 3.1.1.5 Invoked Functions; 3.1.2 Minimize Function Call Overheads; 3.1.3 Employ Early Bail-Outs; 3.1.4 Simplify Loop Contents; 3.1.5 Unroll Simple Loops; 3.1.6 Optimize Nested Loops; 3.1.7 Switch the Order of Nested Loops; 3.1.8 Minimize Dereferencing; 3.1.9 Postpone I/O and Graphics Until the Loop Ends; 3.1.10 Merge or Split Loops; 3.1.11 Loop Over the Shorter Dimension; 3.1.12 Run Loops Backwards; 3.1.13 Partially Optimize a Loop; 3.1.14 Use the Loop Index Rather than Counters; 3.1.15 MATLAB’s JIT; 3.2 Data Caching; 3.2.1 Read-Only Caches; 3.2.2 Common Subexpression Elimination; 3.2.3 Persistent Caches; 3.2.3.1 In-Memory Persistence; 3.2.3.2 Non-Memory Persistence; 3.2.4 Writable Caches; 3.2.4.1 Initializing Cache Data; 3.2.4.2 Memoization; 3.2.4.3 Multilayered (Offline) Cache; 3.2.5 A Real-Life Example: Writable Cache; 3.2.6 Optimizing Cache Fetch Time; 3.3 Smart Checks Bypass; 3.4 Exception Handling; 3.5 Improving Externally Connected Systems; 3.5.1 Database; 3.5.1.1 Design; 3.5.1.2 Storage; 3.5.1.3 Indexing; 3.5.1.4 Driver and Connection; 3.5.1.5 SQL Queries; 3.5.1.6 Data Updates; 3.5.2 File System and Network; 3.5.3 Computer Hardware; 3.6 Processing Smaller Data Subsets; 3.6.1 Reading from a Database; 3.6.2 Reading from a Data File; 3.6.3 Processing Data; 3.7 Interrupting Long-Running Tasks; 3.8 Latency versus Throughput; 3.8.1 Lazy Evaluation; 3.8.2 Prefetching; 3.9 Data Analysis; 3.9.1 Preprocessing the Data; 3.9.2 Controlling the Target Accuracy; 3.9.3 Reducing Problem Complexity; 3.10 Other Techniques; 3.10.1 Coding; 3.10.1.1 Recursion; 3.10.1.2 Using Known Computational Identities; 3.10.1.3 Remove Unnecessary Computations ("Dead-Code" Elimination); 3.10.1.4 Optimize Conditional Constructs; 3.10.1.5 Use Short-Circuit Conditionals (Smartly!); 3.10.1.6 Multiply Rather than Divide (or Not); 3.10.2 Data; 3.10.2.1 Optimize the Processed Data; 3.10.2.2 Select Appropriate Data Structures; 3.10.2.3 Utilize I/O Data Compression; 3.10.3 General; 3.10.3.1 Reduce System Interferences; 3.10.3.2 Self-Tuning; 3.10.3.3 Jon Bentley’s Rules; ; 4. MATLAB® -Specific Techniques; 4.1 Effects of Using Different Data Types; 4.1.1 Numeric versus Nonnumeric Data Types; 4.1.2 Nondouble and Multidimensional Arrays; 4.1.3 Sparse Data; 4.1.4 Modifying Data Type in Run Time; 4.1.5 Concatenating Cell Arrays; 4.1.6 Datasets, Tables, and Categorical Arrays; 4.1.7 Additional Aspects; 4.2 Characters and Strings; 4.2.1 MATLAB’s Character/Number Duality; 4.2.2 Search and Replace; 4.2.3 Converting Numbers to Strings (and Back); 4.2.4 String Comparison; 4.2.5 Additional Aspects; 4.2.5.1 Deblanking; 4.2.5.2 Concatenating Strings; 4.2.5.3 Converting Java Strings into MATLAB; 4.2.5.4 Internationalization; 4.3 Using Internal Helper Functions; 4.3.1 A Sample Debugging Session; 4.4 Date and Time Functions; 4.5 Numeric Processing; 4.5.1 Using inf and NaN; 4.5.2 Matrix Operations; 4.5.3 Real versus Complex Math; 4.5.4 Gradient; 4.5.5 Optimization; 4.5.6 Fast Fourier Transform; 4.5.7 Updating the Math Libraries; 4.5.8 Random Numbers; 4.6 Functional Programming; 4.6.1 Invoking Functions; 4.6.1.1 Scripts versus Functions; 4.6.1.2 Function Types; 4.6.1.3 Input and Output Parameters; 4.6.1.4 Switchyard Functions Dispatch; 4.6.2 onCleanup; 4.6.3 Conditional Constructs; 4.6.4 Smaller Functions and M-files; 4.6.5 Effective Use of the MATLAB Path; 4.6.6 Overloaded Built-In MATLAB Functions; 4.7 Object-Oriented MATLAB; 4.7.1 Object Creation; 4.7.2 Accessing Properties; 4.7.3 Invoking Methods; 4.7.4 Using System Objects; 4.8 MATLAB Start-Up; 4.8.1 The MATLAB Startup Accelerator; 4.8.2 Starting MATLAB in Batch Mode; 4.8.3 Slow MATLAB Start-Up; 4.8.4 Profiling MATLAB Start-Up; 4.8.5 Java Start-Up; 4.9 Additional Techniques; 4.9.1 Reduce the Number of Workspace Variables; 4.9.2 Loop Over the Smaller Data Set; 4.9.3 Referencing Dynamic Struct Fields and Object Properties; 4.9.4 Use Warning with a Specific Message ID; 4.9.5 Prefer num2cell Rather than mat2cell; 4.9.6 Avoid Using containers.Map; 4.9.7 Use the Latest MATLAB Release and Patches; 4.9.8 Use is* Functions Where Available; 4.9.9 Specify the Item Type When Using ishghandle or exist; 4.9.10 Use Problem-Specific Tools; 4.9.11 Symbolic Arithmetic; 4.9.12 Simulink; 4.9.13 Mac OS; 4.9.14 Additional Ideas; ; 5. Implicit Parallelization (Vectorization and Indexing); 5.1 Introduction to MATLAB Vectorization; 5.1.1 So What Exactly is MATLAB Vectorization?; 5.1.2 Indexing Techniques; 5.1.3 Logical Indexing; 5.2 Built-In Vectorization Functions; 5.2.1 Functions for Common Indexing Usage Patterns; 5.2.2 Functions That Create Arrays; 5.2.3 Functions That Accept Vectorized Data; 5.2.3.1 reshape; 5.2.4 Functions That Apply Another Function in a Vectorized Manner; 5.2.4.1 arrayfun, cellfun, spfun, and structfun; 5.2.4.2 bsxfun ; 5.2.5 Set-Based Functions; 5.3 Simple Vectorization Examples; 5.3.1 Trivial Transformations; 5.3.2 Partial Data Summation; 5.3.3 Thresholding; 5.3.4 Cumulative Sum; 5.3.5 Data Binning; 5.3.6 Using meshgrid and bsxfun ; 5.3.7 A meshgrid Variant; 5.3.8 Euclidean Distances; 5.3.9 Range Search; 5.3.10 Matrix Computations; 5.4 Repetitive Data; 5.4.1 A Simple Example; 5.4.2 Using repmat Replacements; 5.4.3 Repetitions of Internal Elements; 5.5 Multidimensional Data; 5.6 Real-Life Example: Synthetic Aperture Radar Matched Filter; 5.6.1 Naïve Approach; 5.6.2 Using Vectorization; 5.7 Effective Use of MATLAB Vectorization; 5.7.1 Vectorization Is Not Always Faster; 5.7.2 Applying Smart Indexing; 5.7.3 Breaking a Problem into Simpler Vectorizable Subproblems; 5.7.4 Using Vectorization as Replacement for Iterative Data Updates; 5.7.5 Minimizing Temporary Data Allocations; 5.7.6 Preprocessing Inputs, Rather Than Postprocessing the Output; 5.7.7 Interdependent Loop Iterations; 5.7.8 Reducing Loop Complexity; 5.7.9 Reducing Processing Complexity; 5.7.10 Nested Loops; 5.7.11 Analyzing Loop
• Edition: 1st
• Publisher Details: Boca Raton : Chapman & Hall/CRC
• Publication Date: 2015
• Extent: 1 online resource, illustrations (black and white, and colour)
• Subjects: 518.028553; Numerical analysis -- Computer programs
• Languages: English
• ISBNs: 9781482211306
• Related ISBNs: 9781482211290
• Notes: Note: Description based on CIP data; item not viewed.
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