Posts by Henry Ho
Customizing Kernels with hipBLASLt TensileLite GEMM Tuning - Advanced User Guide
- 06 April 2026
Optimizing General Matrix Multiply (GEMM) operations is critical for maximizing the efficiency of AI models on AMD hardware. In our previous blog posts, we explored Offline Tuning, a method for selecting the best-performing kernel from an existing solution pool. For detailed instructions on using hipBLASLt-bench, please refer to hipBLASLt offline tuning part 1 and part 2. Additionally, for a streamlined experience, check out the Day 0 Developer Guide: hipBLASLt Offline GEMM Tuning Script which covers one-click offline tuning. Furthermore, for scenarios requiring dynamic runtime adaptation, developers can explore our recently published blog on hipBLASLt Online GEMM Tuning.
Measuring Max-Achievable FLOPs – Part 2
- 28 February 2025
In our previous blog post, we explored the conceptual differences between Peak FLOPs and Max-Achievable FLOPs (MAF), explaining why the gap between these metrics has widened with modern ML-optimized hardware. This second installment provides a detailed methodology for measuring MAF on AMD GPUs, including the specific environmental conditions, matrix size optimization techniques, and tools required for accurate measurement. We present the actual MAF results for AMD Instinct MI300X and MI325X GPUs across different precision formats (FP16, BF16, and FP8) along with their corresponding median frequencies. We also explain how software efficiency and frequency management impact MAF, and demonstrate why boost clock capabilities remain important for latency-sensitive workloads such as LLM inference with small batch sizes.