Imagine you're designing a program that has to crunch massive amounts of data, render realistic 3D scenes, or power AI models—doing all that sequentially would take ages. That’s where compute programming comes in. I see it as unlocking parallel power: instead of one worker doing all the tasks, it’s like hiring a whole crew to tackle things simultaneously. This approach—where tasks are split across many cores or even specialized processors like GPUs—is radically changing how we build software. It makes everything faster, smarter, and way more scalable. The shift to parallelism isn’t just a performance tweak; it’s a fundamental leap forward in how we think about programming. And what’s really exciting is the rise of tools like WebGPU. I’ve been exploring how it lets developers tap directly into the massive parallel power of modern graphics cards, even right from a web browser. It’s like giving web apps superhero strength. We’re entering a new era where compute programming isn’t just for specialists—it’s becoming approachable, standardized, and wildly powerful. From gaming to AI to scientific simulations, this is a game changer that’s reshaping what’s possible in modern computing. If this is the future, I’m absolutely here for it.

1. Compute Programming
Parallel Power for Modern Computing
Course CP293

2. Road Map • Why Compute Matters Today
• Compute Languages/API
• Core Concepts & Rules
• Essential Algorithms
• Performance Considerations
• Practical Examples
• Future of Compute
• Summary & Resources

3. Recommend Reading
101 WebGPU and WGSL
Programming Projects (Paperback)
WebGPU Data
Visualization
Cookbook (2nd
Edition)
Lots of projects to keep you busy with compute programming
(working and thinking in parallel)
WebGPU
Compute
(Paperback)

4. Why Learn Compute Programming Today?
• AI/ML revolution demands GPU compute
• Games and simulations are more complex
• Scientific computing needs more power
• WebGPU brings GPU compute to the browser

5. Did You Know?
Modern GPUs contain thousands of cores
while CPUs typically have 4-32 cores

6. What exactly is
compute
programming?

7. What exactly is compute programming?
• Specialized programming for parallel processing
• Utilizes GPU's architecture for general computation
• Different paradigm from traditional CPU programming
• Used in ML, scientific computing, data processing

8. What makes GPU
compute different
from CPU
programming?

9. • Massively parallel vs sequential thinking
• Different memory hierarchy
• Specialized languages (WGSL, CUDA)
• Different performance characteristics
What makes GPU compute different from
CPU programming?
JIM, It’s programming, but not as we know it!
GPU
CPU
Switch ‘Mindset’

10. Hands-On Task
Visit https://webgpulab.xbdev.net and:
• 1. Check your browser's WebGPU support
• 2. Run the demo compute shaders
• 3. Note the performance metrics

11. WebGPU Fact
WebGPU provides 3x better
performance than WebGL for
compute workloads

12. What are the key components of WebGPU?

13. What are the key components of WebGPU?
• Device: Represents the GPU
• Queue: Command submission
• Buffers: Memory storage
• Textures: Image data
• Bind Groups: Resource binding
• Pipelines: Shader configurations

14. Debugging Challenge
Try this broken compute shader:
1. Identify why it fails
2. Fix the memory access issue
3. Measure the performance impact

15. Performance Fact
Coalesced memory access can
improve performance by 20x
compared to random access

16. What are some common compute
programming pitfalls?

17. What are some common compute
programming pitfalls?
• Race conditions between threads
• Incorrect memory barriers
• Poor workgroup sizing
• Memory access conflicts
• Insufficient parallelism

18. Optimization Puzzle
Given this matrix multiplication shader:
• 1. Identify the bottleneck
• 2. Implement tiling with shared memory
• 3. Compare performance before/after

19. Algorithm Fact
Parallel reduction can accelerate
summation operations by 100x on
GPUs

20. What are essential compute algorithms to
learn?

21. What are essential compute algorithms to
learn?
• Parallel reduction (sum, min, max)
• Prefix sum/scan
• Matrix operations
• Sorting (bitonic, radix)
• Image processing kernels
• Physics simulations

22. Implementation Challenge
Implement a parallel histogram:
• 1. Start with naive version
• 2. Add atomic operations
• 3. Optimize with shared memory

23. Optimization
Fact
Proper workgroup sizing can
improve performance by 5-10x

24. How do I profile and
optimize compute
shaders?

25. How do I profile and optimize compute
shaders?
• Use timestamp queries
• Profile different workgroup sizes
• Analyze memory access patterns
• Check for thread divergence
• Compare against theoretical limits

26. Profiling Task
Profile this shader with:
1. Different workgroup sizes (8, 16, 32, 64)
2. Different memory layouts
3. Create a performance report

27. Advanced Fact
Wavefront operations can
enable new optimization
techniques in compute shaders

28. How does compute
programming
integrate with
graphics?

29. How does compute programming integrate
with graphics?
• Compute can prepare data for rendering
• Post-processing effects
• Procedural content generation
• Physics simulations
• Hybrid rendering techniques

30. Research Project
Investigate how:
1. Ray tracing uses compute
2. ML upscaling works
3. Particle systems are optimized

31. Future Fact
WebGPU is expected to
enable browser-based CAD
applications and scientific
visualization

32. What's next for
compute
programming?

33. What's next for compute programming?
• Tighter ML integration
• More hardware features exposed
• Better debugging tools
• Cross-platform standardization
• More accessible APIs

34. Challenge
Build your first compute shader:
• Simple image processor
• Parallel array reducer
• Interactive simulation

35. • Compute enables massive parallelism
• WebGPU brings GPU compute to the web
• Different mindset from CPU programming
• Performance requires careful optimization
• Essential for modern applications
Summary
Resources Visit
https://xbdev.net
https://webgpulab.xbdev.net

36. Questions

37. Recommend Reading
101 WebGPU and WGSL
Programming Projects (Paperback)
WebGPU Data
Visualization
Cookbook (2nd
Edition)
Lots of projects to keep you busy with compute programming
(working and thinking in parallel)
WebGPU
Compute
(Paperback)