Clement Lin#
Clement Lin is a Senior Member of Technical Staff on AMD’s Data Center GPU team. He is interested in technologies that advance the ROCm ecosystem, focusing on GPU computing for AI workloads and large language models (LLMs), as well as system-level performance tuning. Clement holds a Ph.D. in Computer Science from National Tsing Hua University and most recently worked as a senior engineer at MediaTek before joining AMD.
Posts by Clement Lin
Serving GLM-5.2-MXFP4 on AMD Instinct™ MI355X: When Prefill Context Parallelism Pays
Learn when prefill context parallelism pays on AMD Instinct MI355X: 43-54% more throughput on long prompts with GLM-5.2-MXFP4, and when it loses.
Iteratively Tuning hipBLASLt TensileLite Kernels: A Smaller Search, a Faster Kernel
Learn why a small iterative search beats a big one-shot sweep for hipBLASLt TensileLite kernels, in less time and with no risk of regression.
Reverse-Engineering hipBLASLt TensileLite Kernels: From Solution Name to a Tuning Config
Pin the pool's best kernel into a TensileLite tuning config by decoding its solution name, so an expanded re-tune can only match or beat it.
Serve Kimi-K2.5-MXFP4 on MI355X with ATOM
Serve Kimi-K2.5-MXFP4 on MI355X with ATOM and gfx950 block-scaled FP4 kernels for optimized LLM inference.
Optimizing MI300X Inter-Chiplet Communication via the RCCL Tuner API
Learn how to build a topology-aware RCCL tuner plugin for MI300X CPX/NPS4 mode and validate it with rccl-tests.
Faster Kimi-K2.5-W4A8 Decoding with EAGLE3 on AMD Instinct™ MI325X
Add EAGLE3 speculative decoding and three MoE/FMHA kernel-tuning patches to Kimi-K2.5-W4A8 inference on AMD Instinct™ MI325X with SGLang, AITER, and FlyDSL.
Further Accelerating Kimi-K2.5 on AMD Instinct™ MI325X: W4A8 & W8A8 Quantization with AMD Quark
Quantize Kimi-K2.5 to W4A8 and W8A8 using AMD Quark and serve on MI325X with FlyDSL and AITER for further inference acceleration.
Customizing Kernels with hipBLASLt TensileLite GEMM Tuning - Advanced User Guide
Master hipBLASLt TensileLite Tuning. Learn to build custom kernels that deliver 150%-250% faster GEMM performance on AMD Instinct™ MI300X GPUs
Accelerating Kimi-K2.5 on AMD Instinct™ MI300X: Optimizing Fused MoE with FlyDSL
Optimize Kimi-K2.5 on AMD MI300X using FlyDSL for fused MoE kernel acceleration. Achieve faster TTFT, TPOT, and throughput with our step-by-step optimization guide.
Adaptive Top-K Selection: Eliminating Performance Cliffs Across All K Values on AMD GPUs
Explore adaptive Top-K on MI300X! See how auto-selection and hardware optimizations like DPP and double buffering drive peak efficiency.
Debugging NaN Results in CK Tile GEMM: A rocgdb Detective Story
Learn GPU kernel debugging with rocgdb through a real case: tracing NaN outputs to a one-character typo in CK Tile GEMM
Avoiding LDS Bank Conflicts on AMD GPUs Using CK-Tile Framework
This blog shows how CK-Tile’s XOR-based swizzle optimizes shared memory access in GEMM kernels on AMD GPUs by eliminating LDS bank conflicts