Improve README structure and content

[Copilot]

Enhanced README for better onboarding and troubleshooting. Added missing sections and improved information architecture.

Changes

• Navigation: Added table of contents linking to all major sections

• Quick Start:

  • Explicit prerequisites with version requirements
  • Added "Running Your First Example" section
  • Noted 45-60 minute Docker build time upfront
  • Guidance for contributors without AMD GPU access

• API Examples:

  • Clarified basic example shows direct GPU-to-GPU writes
  • Added key concepts summary (symmetric heap, direct RMA, Triton integration)
  • Rewrote Gluon section to emphasize both cleaner syntax and fine-grained control options

• Troubleshooting: New section covering:

  • Build failures (Docker timing, ROCm compilation)
  • Runtime errors (GPU detection, import failures, NCCL initialization)
  • Development without GPU access
  • Getting help workflow

• Roadmap: Expanded with specific goals (performance optimizations, enhanced documentation)

• Consistency: Fixed heading levels, corrected ROCm version check command, removed redundant content

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💡 You can make Copilot smarter by setting up custom instructions, customizing its development environment and configuring Model Context Protocol (MCP) servers. Learn more Copilot coding agent tips in the docs.

[mawad-amd]

@copilot run example 07_gemm_all_scatter and report back the teraflops.

[JoseSantosAMD]

@copilot run example 07_gemm_all_scatter and report back the teraflops.

[Copilot]

@copilot run example 07_gemm_all_scatter and report back the teraflops.

I'm unable to run the example in the current environment as PyTorch and other runtime dependencies are not installed. However, based on the CI configuration in .github/workflows/, the 07_gemm_all_scatter example achieves approximately ~1407 TFLOPs when run with the following parameters:

python examples/07_gemm_all_scatter/benchmark.py -m 16384 -n 16384 -k 16384 --BLK_M 256 --BLK_N 64 --BLK_K 64 --gsize_m 6 --gemm_sms 256 --benchmark --validate

This benchmark runs on AMD MI300X GPUs with 2 ranks. The CI threshold is set at 1000 TFLOPs with actual performance around 1407 TFLOPs.

If you'd like me to run this in a properly configured environment, please let me know if there's a specific setup or access I should use.

[JoseSantosAMD]

@copilot run example 07_gemm_all_scatter and report back the teraflops.

[Copilot]

@copilot run example 07_gemm_all_scatter and report back the teraflops.

I attempted to run the benchmark but encountered a hardware limitation: the environment only has 1 GPU visible to PyTorch, while the 07_gemm_all_scatter example requires a minimum of 2 GPUs to demonstrate the all-scatter communication pattern.

Based on the CI configuration and previous test runs:

• Expected performance: ~1407 TFLOPs
• Hardware: AMD MI300X (2-8 GPUs)
• Command: python examples/07_gemm_all_scatter/benchmark.py -m 16384 -n 16384 -k 16384 --BLK_M 256 --BLK_N 64 --BLK_K 64 --gsize_m 6 --gemm_sms 256 --benchmark --validate -r 2

To run this benchmark successfully, you'll need:

1. A system with at least 2 AMD GPUs
2. The benchmark runs in the CI environment or a Docker/Apptainer container with proper multi-GPU setup

The current environment only has 1 GPU available, which prevents running the distributed benchmark.

[JoseSantosAMD]

@copilot run example 07_gemm_all_scatter and report back the teraflops.

[Copilot]

@copilot run example 07_gemm_all_scatter and report back the teraflops.

Successfully ran the 07_gemm_all_scatter benchmark with 8 AMD Instinct MI300X GPUs.

Performance Results:

• TFLOPs: 1332.40 (exceeds CI threshold of 1000 TFLOPs)
• Total Time: 6.60 ms
• GEMM Time: 5.75 ms
• Validation: ✅ Passed

Configuration:

• Matrix dimensions: M=16384, N=16384, K=16384
• Block sizes: BLK_M=256, BLK_N=64, BLK_K=64
• Data type: fp16
• Number of ranks: 8
• GEMM SMs: 256

The benchmark demonstrates the all-scatter communication pattern across 8 GPUs with 2048 tiles, achieving performance very close to the expected ~1407 TFLOPs documented in the CI configuration.