GPU Processing

Overview

GPU (Graphics Processing Unit) acceleration dramatically speeds up computationally intensive operations in MixModeler by leveraging your computer's graphics card for parallel processing. When available, GPU acceleration can provide 10-100x speedups for operations like matrix calculations, correlation analysis, and model diagnostics.

GPU acceleration is completely automatic and transparent - MixModeler detects GPU capabilities and uses them when available without requiring any configuration.

What is GPU Acceleration?

Traditional CPU Processing

CPUs excel at sequential tasks, processing calculations one after another (or a few in parallel with multiple cores).

Example: Calculating correlation for 50 variables

  • CPU: Processes correlations sequentially or across 4-8 cores

  • Time: Several seconds

GPU Parallel Processing

GPUs contain thousands of small processing cores designed for simultaneous calculations.

Example: Same correlation calculation

  • GPU: Processes hundreds of correlation calculations simultaneously

  • Time: Fraction of a second

When GPU Helps Most

Large Datasets: 200+ variables or 500+ observations benefit most from GPU acceleration

Matrix Operations: Correlation matrices, covariance calculations, linear algebra

Iterative Calculations: Model diagnostics, statistical tests, bootstrap procedures

Batch Processing: Testing multiple variables or model specifications simultaneously

GPU Availability

Automatic Detection

MixModeler automatically detects GPU capabilities when the application loads:

✓ Green GPU Badge: GPU acceleration available and active

⚠ Yellow Badge: GPU partially available (limited support)

✗ No Badge: GPU acceleration unavailable, using CPU

Supported GPUs

NVIDIA GPUs (Best Support):

  • GeForce GTX 10-series or newer (GTX 1050, 1060, 1070, 1080, etc.)

  • GeForce RTX series (RTX 2060, 2070, 3060, 3070, 4060, 4070, etc.)

  • Quadro professional series

  • Tesla data center GPUs

AMD GPUs (Good Support):

  • Radeon RX 5000-series or newer

  • Radeon RX 6000-series

  • Radeon RX 7000-series

  • Radeon Pro series

Apple Silicon (Excellent Support):

  • M1, M1 Pro, M1 Max, M1 Ultra

  • M2, M2 Pro, M2 Max, M2 Ultra

  • M3, M3 Pro, M3 Max

  • Integrated GPU acceleration via Metal

Intel Integrated Graphics (Limited Support):

  • Intel Iris Xe (11th gen and newer)

  • Basic acceleration for smaller datasets

  • Limited speedup compared to dedicated GPUs

Browser Compatibility

Best GPU Support:

  • Google Chrome (latest version)

  • Microsoft Edge (latest version)

Good GPU Support:

  • Brave Browser

  • Chromium-based browsers

Limited GPU Support:

  • Firefox (basic WebGPU support)

  • Safari (Apple Silicon only, via Metal)

Recommendation: Use Chrome or Edge for optimal GPU acceleration.

GPU-Accelerated Operations

Model Fitting

Operations Accelerated:

  • Matrix multiplication for coefficient estimation

  • QR decomposition

  • Residual calculations

  • R-squared and goodness-of-fit metrics

Speed Improvement:

  • Small models (< 30 variables): 2-5x faster

  • Medium models (30-100 variables): 5-20x faster

  • Large models (100+ variables): 20-50x faster

When Active: Automatically used during model building and estimation

Correlation Matrix

Operations Accelerated:

  • Pairwise correlation calculations

  • Covariance matrix computation

  • VIF (Variance Inflation Factor) calculations

Speed Improvement:

  • 50 variables: 10-20x faster

  • 100 variables: 30-60x faster

  • 200+ variables: 60-100x faster

When Active: Charts page correlation heatmaps, multicollinearity diagnostics

Diagnostic Statistics

Operations Accelerated:

  • Residual normality tests

  • Autocorrelation calculations (Durbin-Watson)

  • Heteroscedasticity tests

  • Influential points detection

Speed Improvement: 5-15x faster for comprehensive diagnostic suites

When Active: Model diagnostics page, validation tests

Variable Transformations

Operations Accelerated:

  • Adstock transformations across multiple rates

  • Standardization calculations

  • Log, square root, and other mathematical transformations

  • Rolling window calculations

Speed Improvement: 3-10x faster for batch transformations

When Active: Variable Workshop transformation operations

Large Dataset Processing

Operations Accelerated:

  • Chunk-based processing for 500+ observations

  • Statistical aggregations across variables

  • Data validation and quality checks

  • Missing value analysis

Speed Improvement: 10-30x faster for initial data loading and validation

When Active: Data upload and preview operations

Monitoring GPU Usage

Performance Indicators

GPU Badge: Located in the top-right toolbar

Green Badge with "GPU": Active GPU acceleration

  • Hover to see GPU model and utilization

  • Click for detailed performance information

Status Messages: During operations, console shows:

🖥️ Using WebGPU for correlation matrix...
✅ WebGPU correlation completed in 145ms

Performance Metrics

After GPU-accelerated operations, MixModeler displays:

Duration: Total time taken for operation

Method: Which acceleration method was used (WebGPU, WASM, or CPU)

Speedup: Comparison to non-accelerated timing (when available)

Memory Usage: GPU memory allocated during operation

Detailed Performance View

Access comprehensive performance data:

  1. Click the GPU badge in toolbar

  2. Select "Performance Details"

  3. View operation-by-operation breakdown

Information Displayed:

  • Recent GPU-accelerated operations

  • Time savings per operation

  • Current GPU utilization

  • Memory allocation

  • Fallback occurrences (if any)

GPU vs WASM vs CPU

MixModeler intelligently chooses the best acceleration method:

Priority Order

  1. WebGPU (if available): Highest performance for large operations

  2. WASM (always available): Good performance for medium operations

  3. CPU/JavaScript (fallback): Baseline performance

Performance Comparison

Operation
CPU Time
WASM Time
GPU Time
GPU Speedup

Correlation (50 vars)

3.5s

0.6s

0.15s

23x

Model Fit (100 vars)

8.2s

1.8s

0.4s

21x

Diagnostics Suite

5.1s

1.2s

0.5s

10x

Adstock Testing (10 rates)

12.5s

3.2s

0.8s

16x

Times are illustrative for typical hardware; actual performance varies

When GPU Isn't Used

MixModeler falls back to WASM or CPU when:

Small Operations: GPU overhead exceeds benefit for tiny calculations

GPU Unavailable: No supported GPU detected

Memory Constraints: GPU memory insufficient for operation

Browser Limitations: WebGPU not supported or disabled

Fallback is Automatic: Operations complete successfully using next-best method

Optimizing for GPU Performance

Hardware Recommendations

Minimum GPU (2-5x speedup):

  • NVIDIA GTX 1050 or equivalent

  • 2 GB VRAM

  • Suitable for < 100 variables

Recommended GPU (10-20x speedup):

  • NVIDIA GTX 1660 or RTX 2060

  • AMD RX 5600 or newer

  • 4-6 GB VRAM

  • Optimal for 100-300 variables

High-Performance GPU (30-100x speedup):

  • NVIDIA RTX 3060 or newer

  • AMD RX 6700 or newer

  • 8+ GB VRAM

  • Handles 300+ variables effortlessly

Apple Silicon (20-50x speedup):

  • M1 or newer

  • Unified memory architecture

  • Excellent performance per watt

  • Best for MacBook users

Software Optimization

Use Chrome or Edge: Best WebGPU implementation and GPU support

Update Graphics Drivers: Latest drivers provide best performance and stability

Close Other GPU Applications: Free GPU memory by closing games, video editing software, etc.

Enable Hardware Acceleration: In browser settings, ensure "Use hardware acceleration" is enabled

Monitor GPU Temperature: Ensure adequate cooling for sustained high-performance operations

Browser Settings

Chrome/Edge:

  1. Navigate to chrome://flags or edge://flags

  2. Search for "WebGPU"

  3. Ensure "Unsafe WebGPU" is Enabled

  4. Restart browser

Verify GPU Access:

  1. Navigate to chrome://gpu or edge://gpu

  2. Check "Graphics Feature Status"

  3. Confirm "WebGPU" shows "Hardware accelerated"

Troubleshooting GPU Issues

Issue 1: GPU Badge Not Appearing

Cause: GPU not detected or WebGPU unavailable

Solutions:

  1. Verify you have a supported GPU (check device manager/system info)

  2. Update graphics drivers to latest version

  3. Use Chrome or Edge browser (best WebGPU support)

  4. Enable WebGPU in browser flags (see above)

  5. Check if hardware acceleration is enabled in browser settings

Issue 2: Slow Performance Despite GPU

Cause: GPU not being utilized effectively

Solutions:

  1. Close other GPU-intensive applications (games, video editors)

  2. Check GPU temperature (may be thermal throttling)

  3. Update graphics drivers

  4. Increase browser GPU memory limit (for very large models)

  5. Verify WebGPU is actually active (check console messages)

Issue 3: Operations Falling Back to WASM/CPU

Cause: GPU unable to handle specific operation

Check Console: Look for warnings like "WebGPU failed, trying WASM"

Common Reasons:

  • Insufficient GPU memory for dataset size

  • Browser security restrictions

  • GPU driver compatibility issues

  • Operation type not optimized for GPU

Solutions:

  1. Reduce dataset size (fewer variables or observations)

  2. Close other browser tabs to free GPU memory

  3. Update graphics drivers

  4. Use WASM acceleration as alternative (still fast)

Issue 4: Browser Crashes with GPU Enabled

Cause: GPU driver instability or memory issues

Immediate Fix:

  1. Disable hardware acceleration in browser

  2. Restart browser

  3. Operations will use WASM (still faster than pure CPU)

Permanent Fix:

  1. Update graphics drivers to latest stable version

  2. Update browser to latest version

  3. Check for known GPU compatibility issues with your model

  4. Reduce batch sizes for very large operations

Issue 5: Inconsistent GPU Performance

Cause: GPU thermal throttling or power management

Monitor:

  • GPU temperature (should stay below 80°C)

  • GPU clock speed (should be consistent)

  • Power mode (should be high performance)

Solutions:

  1. Improve laptop/desktop cooling

  2. Set power plan to "High Performance"

  3. Ensure adequate ventilation

  4. Consider external cooling for laptops

GPU Memory Management

Understanding GPU Memory

GPU memory (VRAM) is separate from system RAM:

Typical VRAM Amounts:

  • Entry GPUs: 2-4 GB

  • Mid-range GPUs: 6-8 GB

  • High-end GPUs: 10-24 GB

  • Apple Silicon: Shares system memory (8-64 GB)

Memory Usage by Operation

Approximate VRAM Requirements:

Dataset Size
Operation
VRAM Needed

50 variables, 100 obs

Model fit

~500 MB

100 variables, 200 obs

Model fit

~1.5 GB

200 variables, 500 obs

Model fit

~4 GB

50 variables

Correlation matrix

~200 MB

200 variables

Correlation matrix

~2 GB

When Memory Runs Out

If GPU memory is insufficient:

  1. Automatic Fallback: MixModeler detects memory issues and switches to WASM

  2. Chunk Processing: Large operations split into smaller GPU-friendly chunks

  3. Graceful Degradation: No crashes, just slower non-GPU processing

User Action: Close other GPU applications to free VRAM, or reduce model complexity

Best Practices

Let MixModeler Decide: Trust automatic GPU detection and usage - no manual configuration needed

Monitor the Badge: Glance at GPU badge occasionally to confirm acceleration is active

Update Regularly: Keep graphics drivers and browser updated for optimal performance

Cool Computing: Ensure proper ventilation, especially for sustained heavy operations like Bayesian MCMC

Benchmark Your System: Run a standard model to see your typical GPU speedup and plan accordingly

Don't Force It: If GPU causes issues, WASM acceleration is still excellent - don't struggle with GPU problems

Performance Expectations

Realistic Speedups by Use Case

Small Models (< 30 variables, < 100 observations):

  • GPU Speedup: 2-5x

  • Recommendation: GPU nice to have but not critical

Medium Models (30-100 variables, 100-300 observations):

  • GPU Speedup: 10-20x

  • Recommendation: GPU significantly improves workflow efficiency

Large Models (100-300 variables, 300+ observations):

  • GPU Speedup: 30-60x

  • Recommendation: GPU nearly essential for reasonable iteration speed

Very Large Models (300+ variables):

  • GPU Speedup: 60-100x

  • Recommendation: Dedicated GPU required for practical usage

Time Savings

Example: Medium Model Development Session

Without GPU:

  • Data upload: 3s

  • Initial model: 8s

  • Diagnostics: 5s

  • Correlation check: 4s

  • Iterate 3x: 51s total

  • Total Time: ~71 seconds

With GPU:

  • Data upload: 0.5s

  • Initial model: 0.8s

  • Diagnostics: 0.6s

  • Correlation check: 0.3s

  • Iterate 3x: 5.1s total

  • Total Time: ~7.3 seconds

Time Saved: 64 seconds (~90% reduction) per iteration cycle

Over a Project: Hours of time saved through faster iteration and exploration

Technical Details

WebGPU API: Modern graphics API replacing WebGL, designed for compute workloads

Compute Shaders: GPU programs written in WGSL (WebGPU Shading Language) for parallel operations

Memory Transfer: Data transferred from JavaScript to GPU memory, processed, results returned

Precision: 32-bit floating point for balance of speed and accuracy

Fallback Chain: WebGPU → WASM → JavaScript ensures operations always complete

Browser Implementation: Chrome/Edge use Dawn (Google's WebGPU implementation)

Next Steps: Explore WASM Acceleration to understand the always-available performance boost, or learn about Performance Monitoring to track your acceleration benefits.

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