[HN Gopher] Optimizing Matrix Multiplication on RDNA3
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       Optimizing Matrix Multiplication on RDNA3
        
       Author : skidrow
       Score  : 35 points
       Date   : 2025-03-25 09:55 UTC (3 days ago)
        
 (HTM) web link (seb-v.github.io)
 (TXT) w3m dump (seb-v.github.io)
        
       | SavageNoble wrote:
       | This is really cool. 60% is no joke and as a 7900XTX owner I
       | would love the performance boost.
       | 
       | Well done!
        
         | astar1 wrote:
         | fwiw, I was curious on the practical implications, as I've been
         | in the market for a gpu for a while, so I asked gemini 2.5 Pro:
         | 
         | * Okay, I understand. Although the specific article didn't
         | detail the implications for LLMs and RAG, we can infer the
         | connections based on how these technologies work and the
         | importance of matrix multiplication (MatMul).
         | 
         | Here's how faster GPU matrix multiplication, like the kind
         | discussed in the article's topic, would practically impact LLM
         | training, inference, and RAG:
         | 
         | Core Concept: Large Language Models (LLMs), which are a core
         | part of both standard LLM usage and RAG systems, rely heavily
         | on matrix multiplication operations. These calculations happen
         | within the transformer architecture, particularly in the
         | attention mechanisms and feedforward layers. This process is
         | computationally intensive and often a performance bottleneck.
         | 
         | Implications:
         | 
         | LLM Training:
         | 
         | -Faster Training: Training large models requires immense
         | amounts of matrix multiplication. Faster MatMul directly
         | translates to significantly reduced training times. This allows
         | researchers and developers to iterate more quickly, experiment
         | with larger models, and potentially lower the overall cost of
         | training.
         | 
         | -Feasibility of Larger Models: Enhanced MatMul speed makes it
         | more computationally feasible to train even larger and more
         | complex language models.
         | 
         | LLM Inference (Generating Responses):
         | 
         | -Lower Latency: When an LLM generates text, it performs
         | numerous matrix multiplications. Faster GPU MatMul leads to
         | quicker calculations, resulting in lower latency (faster
         | response times) for users interacting with the LLM. This is
         | crucial for real-time applications.
         | 
         | -Higher Throughput: More inferences can be processed in the
         | same amount of time on the same hardware, improving the
         | efficiency and scalability of LLM deployment.
         | 
         | -Running Larger Models: Efficient MatMul can make it possible
         | to run larger, more capable models on existing or less powerful
         | hardware than would otherwise be required.
         | 
         | Retrieval-Augmented Generation (RAG):
         | 
         | -Faster Generation: RAG systems use an LLM (the generator) to
         | synthesize answers based on retrieved information. All the
         | inference benefits mentioned above (lower latency, higher
         | throughput) apply directly to the generator component of RAG.
         | 
         | -Potentially Faster Retrieval: Some retrieval methods within
         | RAG, like those using Maximum Inner Product Search (MIPS) on
         | embeddings, rely on operations related to matrix multiplication
         | (vector-matrix multiplication). Faster GPU execution of these
         | operations could speed up the retrieval step, although RAG's
         | main bottleneck is often the LLM generation part.
         | 
         | In essence, faster GPU matrix multiplication is a fundamental
         | improvement that ?significantly enhances the speed, efficiency,
         | and scalability of both training and deploying large language
         | models, including those used within RAG systems.*
        
       | almostgotcaught wrote:
       | > Furthermore, performing custom ISA optimizations makes these
       | changes RDNA3-specific
       | 
       | this is overblown at least wrt forward compatibility - all of the
       | instructions used are in RDNA4 and most of them are even in CDNA3
       | (CDNA4 isn't public yet?) and the ones that aren't exactly there
       | are only slightly renamed (ds_load -> ds_read). Sure it's
       | annoying but it's not the end of the world to have some `#ifdef`s
       | in your code (that's not very much different from what the
       | compiler itself is going to do anyway).
        
       | randomNumber7 wrote:
       | Is the author a genius or has AMD questionable software?
        
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