Optimizing Compile Times For Large Latex Documents

The Problem: Why LaTeX Compile Times Increase

As LaTeX documents grow in length and complexity, users often find that document compilation takes longer and longer. Multiple minute-long compile times can severely impact productivity and disrupt the authoring workflow. Understanding why large documents slow down compilation is key to optimizing performance.

At a fundamental level, increased compile times stem from LaTeX having more content to process and typeset. Factors like a high number of included packages, large quantity of images and tables, and overall document length all tax LaTeX's typesetting engine and result in longer compiles. As more content is added, the compiler must do more work analyzing document structure, resolving package dependencies, typesetting text and mathematical equations, placing figures and tables, and finally outputting the fully formatted document.

Identifying the specific factors that scale with document size is essential for pinpointing areas to optimize. As LaTeX users, being strategic about how we author and structure documents can go a long way toward faster compiles. The compile process can be accelerated through compiler choice, structural changes, and targeted content optimizations.

Fundamental Causes Behind Slow Compile Times

Under the hood, LaTeX uses highly optimized compiling engines to analyze source files and typeset structured documents. However, as more content is added, compile times inevitably increase due to algorithms with higher time complexity. The core factors that affect compilation performance are:

Number of Packages and Dependencies

The more packages that are imported in the LaTeX preamble, the longer it takes the compiler to process them. Packages contain useful macros, environments, and style modifications. However they also have a computational overhead during compilation.

Packages may also contain dependencies on other packages behind the scenes. Dependency graphs with too many edges can take substantial time to resolve. Critically evaluating each package and eliminating any unnecessary ones can potentially reduce compile times.

Image and Table Counts

High image and table quantity also scales compile time. For image formats like JPEG, PNG, and PDF, LaTeX has to read in the binary data and correctly embed the raster graphics content into the final document.

For tables, the compiler analyzes tabular environments row-by-row and fits content into the designated columns. Having hundreds of tables and figures for LaTeX to precisely place occupies significant compile time. Reducing count where possible helps the compiler focus efforts on other tasks.

Document Length and Complexity

As a document grows longer, the compiler has more text content to shape into paragraphs, more equations and mathematical expressions to typeset, more syntax constructs to parse like lists, footnotes, cross references, etc. Supporting structured writing inherently carries compile overhead.

Increased use of formatting like multi-level enumerations and itemizations, nested figure/table environments, sectioning depth also taxes the compiler with additional work. Finding ways to simplify document structure and design can help the compiler focus efforts.

Strategies to Optimize Compile Performance

With an understanding of the key factors that increase compile times, authors can employ targeted strategies to alleviate bottlenecks. Optimization techniques focus on streamlining the compiler toolchain itself, reducing package overhead, simplifying document structure and organization, strategizing image handling, and other general performance improvements.

Leveraging LaTeX Compile Tools

The core LaTeX distribution offers basic compilation capabilities. However, supporting tools like latexmk provide advanced functionality that can automatically manage the compile process.

Understanding LaTeX Engines and Compilers

At the foundation, LaTeX relies on TeX typesetting engines like pdfTeX. Formatting outputs to PDF requires running LaTeX, generating auxiliary files, indexing cross-references, and finally using engines like pdfTeX to render the output.

Tools like latexmk handle this entire workflow behind the scenes. Latexmk automatically runs the required programs in sequence as many times as needed to resolve all references. It also determines which compiles are actually necessary avoids unnecessary runs.

Using latexmk for Automatic Compiles

Latexmk offers customizable compile automation that saves significant user effort. It determines the appropriate engine for the desired output format and handles the intricate details of compilation workflow.

Some key advantages include:

- Automatically resolving references and table of contents
- Adding only the minimum runs needed for convergence
- Background compilation support avoids blocking the UI
- Compiler choice flexibility with pdfTeX, XeTeX, LuaTeX support
- Built-in clean up of auxiliary files after compilation

Latexmk enables authors to focus on content while it manages efficient compiling in the background. Configuring the latexmk process is also simpler than manually running tools like pdflatex or bibtex. Latexmk is highly recommended for larger documents to reduce hands-on compile overhead.

Reducing Package Load

Packages are invaluable for LaTeX. However, they do increase compilation load. Two effective ways to streamline package usage:

Being Selective With Packages

A careful eye when examining package necessity can reveal many that influence only aesthetic changes. These contribute to added compile time without deeply altering document structure.

Developing a lean set of essential packages for functionality needs limits overhead. For example, condensing multiple math packages like amsmath, nccmath, mathtools down to only amsmath prunes away unneeded complexity.

Consolidating Math Packages

On a related note, combining functionality from similar packages reduces package count. As an illustration, siunitx handles numeric and scientific formatting but bussproofs provides proof notation. Using the former while foregoing the latter again simplifies compilation dependencies.

Weighing the compile overhead against macro usage on a per-package basis highlights unnecessary bulk. Streamlining packages, particularly math-related ones, directly speeds up document build times.

Simplifying Document Structure

Authoring style and organization choices structurally bloat LaTeX documents. Conscious simplification of formatting and modularization of content significantly lightens compilation workload.

Modularizing Into Multiple Files

Monolithic single-file documents overwhelm compilers, particularly with extreme length. Splitting into logical portions stored as individual files helps in multiple ways:

- Isolates sections to only compile needed content
- Reduces compiler string processing on a per-file basis
- Enables partial builds during authoring vs full builds
- May allow parallel compilation of independent files

Sectioning via \input{} statements is relatively seamless while bringing structural and compile gains. The key suites switching to a multiple file workflow.

Limiting Nested Environments

Heavily nested document elements present obstacles for LaTeX engines to overcome. For example, enumerations with sub-levels strains the structural parsing capabilities. Deeply stacked tables and figures also hamper internal placement logic.

Formatting structures with controlled, shallow nesting complements file modularization. Together, flattened content arrangement paired with segmentation across files promote efficient compilation.

Handling Images and Tables

Table and figure elements require special handling by the LaTeX compiler during typesetting. Optimizing storage format reduces overhead of embedding graphical assets.

Converting Vector Images to PDF

Vector graphics like charts and diagrams with EPS, AI, and SVG formats require additional processing to rasterize and embed into PDF. Native PDF images streamline this pipeline.

Vector images contain mathematical descriptions of lines and curves rather than pixels. LaTeX has to internally render these before integrating into output pages. Pre-rasterizing to PDF saves significant effort.

Using Lightweight Table Generators

The traditional LaTeX tabular environment forces compilers to calculate column widths, align cells, apply formatting line-by-line down table rows. This quadrant-based alignment occupies substantial effort.

Alternative packages like tabu and tabularx determine alignments globally reducing parses of tabular content. Similarly, markdown-based Pandoc tables require only style typesetting after fast conversion. Leveraging these tools simplifies compile work.

Other Compile Optimizations

Further techniques like enabling PDF compression and cleaning auxiliary files also assist compilers.

Enabling PDF Compression

Output PDFs can be configured to enable lossless size compression. Though the compiler must expend additional effort during output generation, the savings in I/O offsets this cost.

Smaller files require less IO processing all around. Enabling compression consequently lightens filesystem load during reads and writes alongside LaTeX duties.

Deleting Auxiliary Files

A byproduct of compilation are numerous auxiliary files containing intermediate data structures, indices, etc. Most are reused only between runs rather than long-term.

Frequently wiping these files clears temporary build artifacts. It nudges LaTeX to rebuild crucial structures vital for typesetting instead of relying on stale data in scratch files. In turn, this focuses compilation on essential tasks.

Benchmarking Performance Improvements

Applying compile optimizations iteratively allows incremental benchmarking and validation of effectiveness. Authors should measure document performance before and after changes using consistent methodology.

Testing Compile Time Before and After

Adding explicit timing around compilation steps measures speedup gained by each improvement. Wrap the LaTeX run command with a timer to quantify gains.

For example, Linux/macOS:

TIMEFORMAT='%3R'; time latexmk fileName

Comparing elapsed times for the documented runs indicates optimization impact. Lower times signify the desired speedup from tweaks.

Identifying Further Areas for Improvement

Reviewing compiler output also highlights portions that still require excessive processing. Warning and log statements may point out heavy tasks related to code macros, external assets, and structural constructs.

These messages assist in pinpointing remaining bottlenecks. They directly suggest next steps that can further reduce large document compile times through targeted mitigation. Iteratively addressing warnings accelerates optimizations.

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