Top 10 Features of chmProcessor You Should Know

Optimizing chmProcessor for Faster CHM File GenerationCreating Compiled HTML Help (CHM) files can be a crucial part of distributing documentation for Windows applications, and chmProcessor is a common toolchain component used to automate and build CHM files from HTML sources. When documentation projects grow — larger HTML sets, many images, CSS, and scripting — build times can increase and slow development cycles. This article outlines practical strategies to optimize chmProcessor-based builds for speed, reliability, and repeatability without sacrificing the quality of the generated CHM.

Why build performance matters

Faster CHM builds shorten the feedback loop for documentation writers, QA, and developers. In CI pipelines and nightly builds, reduced build time lowers resource usage and accelerates deployment. Optimizing the pipeline also reduces developer frustration and enables more frequent, smaller documentation updates.

Understand the chmProcessor workflow

Before optimizing, map the typical steps chmProcessor follows in your setup:

Preprocess HTML sources (templating, includes, markdown-to-HTML conversion).
Copy assets (images, CSS, JS) into a staging folder.
Run chmProcessor to compile the staged HTML into a CHM file — this often invokes Microsoft’s HTML Help Workshop (hhc.exe) or an equivalent compiler.
Postprocessing (signing, packaging, uploading).

Bottlenecks usually appear during preprocessing asset handling and when invoking the CHM compiler repeatedly or on large inputs.

Profiling to find real bottlenecks

Don’t guess — measure. Typical profiling steps:

Time the full build and each sub-step using simple timestamps or a build-tool timer.
Run builds with and without preprocessing steps (templating, minification) to isolate slow tasks.
Monitor disk I/O and CPU during builds (tools: Windows Resource Monitor, Process Explorer).
In CI, compare container start-up time vs. actual build time.

Record findings across multiple runs to account for caching or external variability.

Reduce input size and complexity

Minify and consolidate CSS and JS. Fewer files reduce file I/O and compiler overhead.
Compress raster images (PNG, JPG) using lossless or visually-lossy tools (pngquant, mozjpeg) to reduce disk transfer times.
Replace large raster images with SVG when practical. SVGs usually compress better for diagrams and scale without multiple raster sizes.
Split very large documentation trees into logical subprojects if your release process permits, compiling only changed modules during iterative development.

Use incremental builds

Fully rebuilding entire CHM files for every small change is inefficient.

Track changed source files (timestamp, checksum) and re-run only the preprocessing and copy steps for changed files.
If your workflow allows, compile partial CHM outputs or modular CHM components rather than a single monolithic CHM. Some help systems allow linking separate CHM files or aggregating at install time.

Example approach:

Maintain a cache directory mirroring the last-staged inputs. On each build, copy only files that differ (rsync-style). This minimizes copy time and file system churn.

Optimize file staging and I/O

Use fast SSDs for build workspace; HDDs are substantially slower for many small files.
Reduce unnecessary file copying: prefer hard links or symlinks when the compiler accepts them. On Windows, symbolic links or NTFS hard links can sometimes help (requires permissions).
When running in containers or CI, mount project volumes as cached or use build caches to avoid re-downloading dependencies each run.

Parallelize preprocessing

Many preprocessing tasks are embarrassingly parallel:

Run image optimizations, HTML templating, and markdown conversions in parallel across CPU cores. Tools like GNU Parallel, task runners (Gulp, npm scripts), or a build system (Make, Ninja) can manage this.
Be careful not to overload disk I/O; test the degree of parallelism that yields best wall-clock time.

Configure and tune the CHM compiler

The underlying compiler (hhc.exe) is often a single-threaded bottleneck. Mitigation strategies:

Reduce the number of input files it must process by consolidating HTML and resource files as noted above.
Keep the table of contents and index files optimized — excessively complex TOC/index structures may slow compilation.
If you use multiple CHM outputs (for modular doc sets), run compiles in parallel on multi-core machines.

Note: hhc.exe itself has limited configuration for performance; focus optimization on inputs.

Cache and reuse intermediate artifacts

Cache preprocessed HTML and optimized assets between runs. If source hasn’t changed, reuse the cached version instead of re-running transformations.
Use content-addressable caches (filename based on checksum) to detect reusable artifacts reliably.
In CI, persist caches between jobs using runner cache mechanisms (Azure Pipelines, GitHub Actions cache, etc.).

Use a faster build environment

Developer machines: use NVMe SSDs, sufficient RAM, and modern CPUs to speed preprocessing and I/O-bound tasks.
CI: choose runners with faster disks and CPUs; avoid low-tier containers that throttle I/O.
Consider running builds inside WSL2 (on Windows) where some file operations can be faster, but benchmark — results vary by setup.

Automate with a robust build system

Move away from ad-hoc scripts to a build system that supports: incremental builds, parallel tasks, dependency tracking, and caching.

Recommended tools: Make, Ninja, Cake (C#), or node-based task runners combined with file-watching.
For larger documentation projects, SCons or Bazel provide strong dependency graphs and caching.

Monitor and repeat

Optimizing is iterative:

Add simple timing logs to the build to detect regressions.
Automate performance regression checks in CI for significant build-time increases.
Keep an eye on external changes (new images, big API docs) that may suddenly increase build time.

Example optimized workflow (concise)

Detect changed sources via checksum.
Parallel preprocess changed files (minify CSS/JS, convert markdown, optimize images).
Sync changed files into staging via rsync or incremental copy.
Run chmProcessor/hhc.exe on staged inputs (parallelize across modules if possible).
Cache staged artifacts and generated CHM outputs for reuse.

Troubleshooting slow builds

If disk I/O is saturated: move to faster storage, reduce parallel file writes.
If CPU is maxed during preprocessing: increase parallelism until diminishing returns or add more CPU.
If the CHM compiler is the bottleneck: reduce file count and complexity, or split outputs.

Security and correctness considerations

Verify image and asset optimizations preserve acceptable visual quality.
Ensure automated parallel tasks do not introduce race conditions. Use atomic writes and temp files with renames.
Validate final CHM files automatically for broken links, images, and TOC correctness before release.

Summary

Optimizing chmProcessor builds combines reducing input complexity, using incremental and cached builds, parallelizing preprocessing, tuning I/O, and choosing a performant environment. Measure first, then apply targeted fixes — small, cumulative improvements deliver the greatest reduction in wall-clock time while keeping builds reliable and reproducible.