WASM Acceleration

Overview

WebAssembly (WASM) acceleration provides consistent 5-10x performance improvements for all users, regardless of hardware. Unlike GPU acceleration which requires specific graphics cards, WASM works automatically in all modern browsers and is always available as a reliable performance boost.

WASM is MixModeler's baseline acceleration technology - it requires no configuration, works on all platforms, and ensures your marketing mix models run significantly faster than pure JavaScript implementations.

What is WebAssembly?

Beyond JavaScript

JavaScript is the native language of web browsers, but it has performance limitations for computationally intensive tasks. WebAssembly is a low-level binary format that runs alongside JavaScript with near-native performance.

JavaScript: Interpreted or just-in-time compiled, flexible but slower

WebAssembly: Pre-compiled binary code, optimized for computational performance

How WASM Accelerates MixModeler

Compiled Numerical Libraries: Core statistical operations compiled from optimized C/C++ code

Efficient Memory Management: Direct memory access without JavaScript overhead

SIMD Instructions: Single Instruction Multiple Data operations process multiple values simultaneously

Predictable Performance: Consistent speed across different browsers and platforms

Universal Availability

No Requirements

WASM acceleration is always available because:

✓ All Modern Browsers Support WASM: Chrome, Edge, Firefox, Safari (2017+)

✓ No Special Hardware Needed: Works on any CPU, any platform

✓ Automatic Activation: Loads and runs without user intervention

✓ Cross-Platform Consistency: Same performance on Windows, Mac, Linux

Supported Environments

Desktop Browsers:

• Chrome/Edge: Excellent support

• Firefox: Excellent support

• Safari: Excellent support

• Opera, Brave, Vivaldi: Excellent support

Operating Systems:

• Windows 10/11: Full support

• macOS 10.13+: Full support

• Linux (all modern distributions): Full support

Processors:

• Intel x86-64: Optimized support

• AMD x86-64: Optimized support

• ARM (including Apple Silicon): Full support

• Other architectures: Standard support

Mobile/Tablet (limited MixModeler support):

• iOS Safari: WASM works but interface limitations

• Android Chrome: WASM works but interface limitations

WASM-Accelerated Operations

Model Fitting

Operations Accelerated:

• Linear regression coefficient estimation

• Ordinary Least Squares (OLS) calculations

• Matrix decomposition (QR, SVD)

• Residual and prediction calculations

• Goodness-of-fit statistics (R², adjusted R²)

Speed Improvement: 5-8x faster than pure JavaScript

Example Timing:

• JavaScript: 4.5 seconds for 50-variable model

• WASM: 0.7 seconds for same model

• Time Saved: 3.8 seconds per model run

Statistical Tests

Operations Accelerated:

• Correlation and covariance calculations

• Variance Inflation Factor (VIF)

• Normality tests (Shapiro-Wilk, Jarque-Bera)

• Heteroscedasticity tests

• Autocorrelation tests (Durbin-Watson)

• Granger causality tests

Speed Improvement: 6-10x faster than JavaScript

Example Timing:

• JavaScript: 3.2 seconds for full diagnostic suite

• WASM: 0.4 seconds for same diagnostics

• Time Saved: 2.8 seconds per diagnostic run

Variable Transformations

Operations Accelerated:

• Adstock transformations (geometric and delayed)

• Standardization and normalization

• Mathematical transformations (log, sqrt, atan)

• Moving averages and rolling windows

• Lead/lag operations

• Date-based splits

Speed Improvement: 4-7x faster than JavaScript

Example Timing:

• JavaScript: 2.1 seconds for 10 adstock rates

• WASM: 0.4 seconds for same transformations

• Time Saved: 1.7 seconds per transformation batch

Data Processing

Operations Accelerated:

• CSV parsing and validation

• Missing value detection

• Outlier analysis

• Descriptive statistics (mean, median, std, quantiles)

• Data quality checks

• Large file chunk processing

Speed Improvement: 3-5x faster than JavaScript

Example Timing:

• JavaScript: 1.8 seconds to process 500 rows × 100 variables

• WASM: 0.5 seconds for same dataset

• Time Saved: 1.3 seconds per data load

Correlation Matrices

Operations Accelerated:

• Pairwise correlation calculations

• Correlation matrix generation

• Distance matrices

• Similarity computations

Speed Improvement: 8-12x faster than JavaScript

Example Timing:

• JavaScript: 5.5 seconds for 50×50 correlation matrix

• WASM: 0.6 seconds for same matrix

• Time Saved: 4.9 seconds per correlation analysis

Performance Characteristics

Typical Speedups

Operation Type	JavaScript Time	WASM Time	Speedup
Simple Model (<20 vars)	1.5s	0.3s	5x
Medium Model (20-60 vars)	4.5s	0.7s	6.4x
Large Model (60-150 vars)	12.0s	1.8s	6.7x
Correlation (50 vars)	5.5s	0.6s	9.2x
Diagnostics Suite	3.2s	0.4s	8x
Adstock Testing (10 rates)	8.5s	1.4s	6.1x
Data Loading (large file)	2.5s	0.6s	4.2x

Timings represent typical performance on mid-range hardware

Consistency Across Platforms

Unlike GPU acceleration which varies significantly by hardware, WASM provides predictable performance:

Windows PC (Intel i5):

• Model fitting: ~0.7s

• Diagnostics: ~0.4s

Mac (Apple M1):

• Model fitting: ~0.6s

• Diagnostics: ~0.3s

Linux Workstation (AMD Ryzen 5):

• Model fitting: ~0.8s

• Diagnostics: ~0.5s

Variance: ±20% across platforms vs ±300% for GPU acceleration

WASM vs Other Methods

Performance Hierarchy

For Small Operations (<10 variables):

1. JavaScript (fastest due to no transfer overhead)

2. WASM (minor overhead)

3. GPU (overhead exceeds benefit)

For Medium Operations (10-100 variables):

1. GPU (when available): 15-30x speedup

2. WASM (always available): 5-10x speedup

3. JavaScript (baseline): 1x

For Large Operations (100+ variables):

1. GPU (when available): 30-100x speedup

2. WASM (always available): 6-12x speedup

3. JavaScript (baseline): 1x

Strategic Role

WASM serves as the reliable middle ground:

Better Than: Pure JavaScript (always faster)

Worse Than: GPU acceleration (when available)

Advantage: Universal availability and predictable performance

Use Case: Ensures all users get substantial acceleration, with GPU providing additional boost when available

How WASM Works

Loading and Initialization

On Application Start:

1. Browser downloads WASM modules (~500KB compressed)

2. Modules are compiled to native machine code

3. Initialization takes ~200ms (one-time cost)

4. WASM engine ready for operations

User Impact: Transparent - happens during app loading

Execution Flow

When Operation Triggered:

1. JavaScript prepares data in typed arrays

2. Data passed to WASM memory space

3. Compiled code executes computation

4. Results returned to JavaScript

5. JavaScript updates UI with results

Overhead: Memory transfer (~10-50ms depending on data size)

Net Benefit: Transfer overhead << computational speedup

Memory Management

Linear Memory: WASM uses contiguous memory block

Efficient Transfer: Typed arrays allow zero-copy sharing when possible

Automatic Cleanup: Memory released after operation completes

No Manual Management: Developer-controlled, user-transparent

Monitoring WASM Usage

Performance Indicators

WASM acceleration is indicated in console logs:

🚀 Using WASM for model fitting...
✅ WASM fitting completed in 0.73s

Confirmation: "WASM" method tag indicates acceleration active

Performance Comparison

After operations, MixModeler may show comparative timing:

With WASM: 0.7 seconds Estimated without WASM: ~4.2 seconds Speedup: 6x faster

Estimates based on operation complexity and historical benchmarks

Always-On Status

Unlike GPU (which may fall back), WASM is always active:

✓ No Badge Needed: WASM is baseline, not optional enhancement

✓ No Fallback Messages: If WASM fails, entire app has issues (extremely rare)

✓ Transparent Operation: Just works without user awareness

Troubleshooting WASM Issues

Issue 1: Slower Than Expected Performance

Cause: WASM may not be loading or executing properly

Diagnosis:

• Check browser console for WASM-related errors

• Verify browser is up to date

• Check if browser has WASM disabled (very rare)

Solutions:

1. Update browser to latest version

2. Clear browser cache and reload

3. Check browser settings don't disable WASM

4. Try different browser (Chrome/Edge recommended)

Issue 2: "WASM Module Failed to Load"

Cause: Network issue or corrupted module download

Solutions:

1. Refresh the page (Ctrl+F5 for hard refresh)

2. Clear browser cache

3. Check internet connection

4. Verify no firewall blocking .wasm file downloads

5. Try different network if on restrictive corporate network

Issue 3: Inconsistent Performance

Cause: Browser resource contention or thermal throttling

Check:

• Other browser tabs using heavy resources

• CPU temperature (thermal throttling can affect WASM)

• System memory availability

Solutions:

1. Close unnecessary browser tabs

2. Close other applications

3. Allow system to cool down

4. Restart browser

Issue 4: Operations Timing Out

Cause: Very large operation exceeding time limits

Note: This is rare - WASM generally handles large operations well

Solutions:

1. Reduce dataset size (remove unnecessary variables/observations)

2. Process in smaller batches

3. Simplify transformations

4. Use faster hardware if available

WASM Limitations

What WASM Doesn't Accelerate

UI Rendering: Chart drawing, interface updates still use JavaScript/browser rendering

Network Operations: Data upload/download not affected by WASM

Small Operations: Overhead can exceed benefit for tiny calculations (<100 data points)

Browser-Specific Features: Some browser APIs not accessible from WASM

When JavaScript is Faster

Very Small Operations: Single variable statistics, simple boolean checks

DOM Manipulation: Updating user interface elements

String Processing: Text manipulation, parsing (though MixModeler uses WASM for CSV parsing)

Async Operations: Waiting for user input, timers, network requests

Rule of Thumb: Operations on <50 data points may run in JavaScript to avoid transfer overhead

Advanced Topics

SIMD Acceleration

Modern WASM includes SIMD (Single Instruction Multiple Data) support:

128-bit SIMD: Process 4 floating-point numbers simultaneously

Operations Benefiting: Vector math, matrix operations, transformations

Speedup Addition: Extra 2-4x on top of base WASM performance

Availability: Chrome 91+, Firefox 89+, Safari 16.4+ (most users have this)

Threading Support

Some WASM modules can use SharedArrayBuffer for multithreading:

Limited Use: Due to browser security restrictions (Spectre mitigations)

When Available: Provides additional 2-4x speedup on multi-core systems

Current Status: MixModeler uses single-threaded WASM for maximum compatibility

Future: Multi-threaded WASM being explored for very large operations

Memory Limits

WASM memory limits by browser:

Chrome/Edge: Up to 4GB WASM memory per tab

Firefox: Up to 2GB WASM memory per tab

Safari: Up to 1GB WASM memory per tab (iOS more restricted)

Practical Impact: Limits apply to datasets with 1000+ variables, extremely rare in MMM

Best Practices

Trust the Automation: WASM works automatically - no user configuration needed

Combine with GPU: When GPU available, MixModeler uses both for maximum speed

Don't Worry About It: WASM is the silent workhorse - just know it's making things faster

Update Browser: Newer browsers have better WASM optimizations

Report Performance: If operations seem unexpectedly slow, check console for WASM messages

Performance Expectations

Realistic Improvements

Small Projects (1-3 models, <30 variables):

• Time Saved: 5-10 minutes per session

• WASM Impact: Noticeable but not critical

Medium Projects (5-10 models, 30-100 variables):

• Time Saved: 20-40 minutes per session

• WASM Impact: Significant productivity improvement

Large Projects (10+ models, 100+ variables):

• Time Saved: 1-2 hours per session

• WASM Impact: Essential for reasonable iteration speed

Bayesian Modeling (MCMC sampling):

• WASM helps with data preparation but MCMC itself uses specialized libraries

• Time Saved: 2-5 minutes on setup, diagnostics, and result processing

Cumulative Benefits

Over a Project Lifecycle:

• Initial exploration: Save 30-60 minutes

• Model development: Save 2-4 hours

• Iteration and refinement: Save 3-6 hours

• Final validation: Save 30-60 minutes

Total Time Saved: 6-12 hours per major MMM project

ROI: Pays for itself immediately in analyst time savings

Technical Details

Language: WASM compiled from optimized C/C++ numerical libraries

Binary Format: Efficient, compact binary encoding (~500KB for statistical modules)

Execution: JIT or AOT compiled to native machine code by browser

Interop: Seamless integration with JavaScript via typed arrays

Security: Sandboxed execution within browser security model

Standards: W3C WebAssembly specification, widely adopted standard

Libraries Used:

• Custom linear algebra routines (matrix operations)

• Statistical test implementations (diagnostics)

• Numerical transformation functions (adstock, standardization)

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Next Steps: Learn about Performance Monitoring to track your acceleration benefits, or explore Large Dataset Handling for optimizing very large models.