Harnessing the Power of LLMs for Software Performance Engineering

By David A. Bader

As someone who has spent decades optimizing parallel algorithms and wrestling with the complexities of high-performance computing, I’m constantly amazed by how the landscape of performance engineering continues to evolve. Today, I want to share an exciting development in our Fastcode initiative: leveraging Large Language Models (LLMs) as sophisticated analysis tools for software performance optimization.

The Multi-LLM Approach: Why Diversity Matters

In our recent work, we’ve been experimenting with an ensemble approach using multiple LLMs — Gemini, Claude, ChatGPT, Grok, Deepseek, and GitHub Copilot —to analyze existing codebases and research literature. This isn’t just about having multiple opinions; it’s about exploiting the unique strengths each model brings to performance analysis.

Each LLM has been trained on different datasets and has distinct architectural biases. Gemini excels at mathematical reasoning that’s crucial for algorithmic complexity analysis. Claude demonstrates remarkable capability in understanding research papers and extracting actionable insights. ChatGPT offers broad programming knowledge across languages and paradigms. Meanwhile, Copilot’s tight integration with development workflows provides practical, immediately implementable suggestions.

From Papers to Performance: Ingesting Research at Scale

One of the most powerful applications we’ve discovered is feeding academic papers directly into these models alongside our source code. The traditional process of manually surveying literature, identifying relevant optimizations, and adapting them to specific codebases is tremendously time-consuming. By having LLMs digest both our implementation and the latest research simultaneously, we’re uncovering optimization opportunities that might have taken months to identify manually.

For instance, when analyzing our triangle counting graph algorithms, we provided the models with both our C code implementation and recent papers on optimizing triangle counting algorithms. The LLMs not only identified specific bottlenecks in our memory access patterns but also suggested concrete modifications based on cutting-edge research we hadn’t yet incorporated.

The Recommendation Engine: Pattern Recognition at Scale

What’s particularly fascinating is how these models excel at pattern recognition across disparate optimization domains. Traditional profiling tools tell us where performance bottlenecks exist, but LLMs help us understand why they exist and how to address them systematically.

The models consistently identify several categories of optimizations: Memory Access Patterns: LLMs excel at recognizing cache-unfriendly access patterns and suggesting data structure reorganizations. They’ve recommended everything from array-of-structures to structure-of-arrays transformations to more sophisticated blocked algorithms.

Algorithmic Alternatives: By cross-referencing our implementations with their vast knowledge of algorithms literature, the models suggest alternative approaches we might not have considered. Sometimes a simple change from a recursive to iterative implementation, or switching from depth-first to breadth-first traversal, yields significant performance gains.

Parallelization Opportunities: Perhaps most relevant to our work, LLMs are surprisingly adept at identifying parallelizable sections of code and suggesting appropriate parallel programming models—whether that’s OpenMP for shared memory, MPI for distributed systems, or GPU kernels for massively parallel workloads.

Challenges and Limitations: Keeping Humans in the Loop

Of course, this approach isn’t without its challenges. LLMs can sometimes suggest optimizations that are theoretically sound but practically infeasible given specific hardware constraints or real-world data characteristics. They might recommend vectorization strategies that don’t account for irregular memory access patterns in graph algorithms, or suggest parallel decompositions that introduce more synchronization overhead than performance benefit.

This is why human expertise remains crucial. The LLMs serve as sophisticated recommendation engines, but the final decisions require deep understanding of the target architecture, the specific problem domain, and the trade-offs involved in each optimization choice.

Validation and Iteration: The Scientific Method Applied

We’ve developed a systematic approach to validating LLM recommendations. Each suggested optimization goes through rigorous benchmarking across multiple datasets and hardware configurations. We’ve found that approximately two-thirds of recommendations lead to measurable performance improvements, which is remarkably high for automated suggestions.

More importantly, even the recommendations that don’t immediately pan out often provide valuable insights that inform subsequent optimization efforts. The models sometimes identify performance anti-patterns we hadn’t recognized, leading to broader improvements in our coding practices.

Looking Forward: The Future of AI-Assisted Performance Engineering

As these models continue to evolve, I envision even more sophisticated applications. Imagine LLMs that can not only suggest optimizations but also predict their performance impact across different architectures, or models that can automatically generate optimized implementations for new hardware targets by learning from existing optimization patterns.

We’re also exploring the potential for LLMs to assist in performance modeling itself—using natural language descriptions of algorithms to generate analytical performance models or even simulation code for complex systems.

Conclusion: Amplifying Human Expertise

The integration of LLMs into our Fastcode workflow isn’t about replacing human expertise—it’s about amplifying it. These models excel at the pattern recognition and literature synthesis that often consume significant time in the optimization process, freeing us to focus on the creative problem-solving and domain-specific insights that only human experts can provide.

As we continue to push the boundaries of computational performance, tools like these will become increasingly essential. The future of software performance engineering lies not in choosing between human intuition and machine analysis, but in combining them effectively to tackle the ever-growing complexity of modern computing systems.

The Fastcode initiative has always been about aggressive optimization through innovative approaches. Incorporating LLMs into our performance engineering toolkit represents the natural evolution of this philosophy—leveraging every available tool to squeeze maximum performance from our computational resources.

https://fastcode.substack.com/p/harnessing-the-power-of-llms-for