Performance & Benchmarks

This page documents how JPDFConvert performs across devices and workloads, the benchmarking methodology we use, practical performance tips, and how to interpret results. Because JPDFConvert runs entirely in the browser, performance is determined primarily by three things: the user’s device (CPU and memory), the browser implementation, and workload characteristics (file sizes, image content, DOCX complexity). Understanding these variables helps you get predictable, fast results.

Why Performance Matters

Speed affects user experience, retention, and accessibility. Users expect conversions to finish within a few seconds for typical documents. Slow conversions cause frustration, abandonments, and higher support load. In resource-constrained environments (low-end phones, older laptops), heavy operations may stall the page or cause the browser to kill the tab. Designing conversion workflows with performance in mind reduces these risks and ensures reliability across a broad user base.

Benchmarking Goals

Our benchmarks aim to answer practical questions:

Test Environment & Methodology

To produce reliable numbers we follow a consistent methodology:

  1. Representative devices: We test across a matrix including an entry-level Android phone (~2–3 years old), a popular mid-range phone, a recent flagship phone, a budget laptop, and a modern desktop. This covers the practical range of user hardware.
  2. Multiple browsers: Chrome (stable), Edge (Chromium), Firefox, and Safari (on iOS/macOS) are used because JS engines and memory management differ significantly.
  3. Warm vs cold runs: Each test is repeated multiple times. The first run measures "cold" load cost (including library initialization); subsequent runs measure steady-state performance.
  4. Controlled files: We use curated test documents: photo-heavy PDFs (many high-res images), mixed-layout DOCX (tables+images), text-heavy PDFs, and long multipage scanned documents.
  5. Metrics collected: elapsed time (ms), peak memory usage (MB, when measurable), CPU utilization (approx), and final output size (MB). We also capture UI responsiveness — whether the page remained interactive during processing.

Representative Results (Summary)

The numbers below are illustrative aggregated medians from our lab tests (October 2025). Actual results vary by device and browser.

Interpreting the Numbers

Two practical takeaways:

  1. Image size dominates: Pixel count matters more than file bytes. A compressed but large-dimension image will use more memory and take longer to render/resample than many small images.
  2. Parsing cost: For DOCX and other structured formats, the parsing stage (DOM-like traversal, style preprocessing) is significant. Clean, simple source files convert faster than heavily styled documents.

Memory Considerations

Browsers enforce memory limits per tab in practice. On mobile devices, available heap might be constrained (often <1GB). When an operation requires more memory than available, the page may become unresponsive or the browser may kill the process. We mitigate this by:

Batching Strategies: Sequential vs. Parallel

Parallel processing can reduce wall-clock time on multicore devices but increases peak memory. For client-side conversion we generally recommend a hybrid approach:

JPDFConvert detects approximate device memory class and defaults to safe sequential processing on constrained devices, while allowing power users to opt into more parallelism.

Optimization Options & Their Impact

We expose a few optimization knobs and measured their relative impact:

Responsive UI & Long-Running Jobs

Maintaining a responsive UI is critical. Blocking the main thread for long tasks causes the whole page to freeze. Our approach:

Device-Specific Tips

For best results, recommend the following to users:

Real-World Example Scenarios

Scenario A — Student: Converting a 15-slide assignment with embedded photos on a mid-range phone. Recommended workflow: batch 3–5 slides at a time, downsample images to 1500px, choose JPEG quality ~80. Expected time: ~6–10s for entire set with a compact output suitable for emailing.

Scenario B — Freelancer: Preparing a portfolio of high-res mockups for print. Use desktop, enable high-fidelity settings (no downsampling, lossless where needed), and generate a separate distribution copy using mild compression for web use. Expected time: desktop conversion completes in seconds; web-ready copy compresses quickly.

Scenario C — Archive of scanned forms: Large scanned PDFs (300 DPI) often inflate size. Use batch OCR + compression: first downsample scans to 300–400 DPI for archival, apply monochrome conversion for purely black-and-white documents, and run OCR to make files searchable. This reduces size and improves utility.

Developer Notes & Instrumentation

Developers integrating JPDFConvert into larger systems should instrument key events:

Note: telemetry should always be opt-in and non-identifying to respect privacy-first principles.

Measuring Performance Locally

If you want to benchmark conversions on your device, follow this simple recipe:

  1. Open an Incognito/Private window to reduce background interference from extensions.
  2. Use a consistent input file set (same images/DOCX) for repeated runs.
  3. Run multiple iterations and take the median time to reduce noise.
  4. Monitor memory in the browser devtools (Performance/Memory panels) to observe peak usage.

Common Performance Pitfalls

Watch out for these frequent causes of poor performance:

Fallback Strategies

When the device cannot complete a requested job, provide graceful fallbacks:

Accessibility & Performance

Accessibility improvements often intersect with performance. For example, adding detailed accessibility tags increases processing, but it's important to preserve those tags. JPDFConvert optimizes tags separately to avoid a large performance hit while maintaining compliance. When working with assistive tech users, prioritize correctness over marginal performance gains.

Future Work & Roadmap

We continue to invest in performance: WebAssembly-accelerated image codecs, smarter heuristics for automatic downsampling, and adaptive concurrency based on device telemetry (opt-in). Future releases aim to push heavy lifting into optimized worker threads and reduce main-thread impact even further.

Practical Checklist Before Conversion

Closing Notes

Performance is a product-quality axis that impacts every user interaction. JPDFConvert's browser-first architecture delivers a privacy-preserving, fast experience for most typical documents. By following the guidelines above and choosing sensible defaults for device class, users and integrators can achieve predictable, high-performance conversions without sacrificing privacy or quality.