CROSS: A Novel Compiler Framework Shatters Irregular Sparse Computation Barriers, Boosting ModelPerformance Multifold
Abstract: The relentless pace of modern AI model developmentnecessitates solutions that enhance computational efficiency without sacrificing accuracy. A significant bottleneck in large-scale AI inference lies in the inefficient processing of unstructured sparse matrices. Thisarticle details CROSS, a groundbreaking end-to-end sparse compilation optimization framework developed by Professor Li Jiang’s team at Shanghai Jiao Tong University’s Advanced ComputerArchitecture Laboratory (IMPACT), with support from the Shanghai Qi Zhi Institute. CROSS achieves significant speedups in fine-grained sparse computation for AI inference, overcoming the limitations of existing solutions.
Introduction: The quest for faster and moreefficient AI inference is a constant challenge. While dense matrix computations benefit from highly optimized libraries like cuBlas, unstructured, fine-grained sparse matrices present a formidable hurdle. Existing sparse operator acceleration libraries and compilation frameworks often struggle with the non-uniformsparsity patterns characteristic of many modern AI models, resulting in suboptimal performance. This inefficiency significantly impacts the speed and scalability of AI applications. CROSS addresses this critical issue by offering a novel approach to sparse computation optimization.
The Challenge of Irregular Sparse Distributions: The core problem lies in the unpredictable distribution ofnon-zero elements within unstructured sparse matrices. Unlike structured sparsity, where patterns are easily predictable and exploitable, irregular sparsity necessitates dynamic and adaptive computation strategies. Traditional methods often resort to general-purpose approaches that fail to leverage the unique characteristics of the sparse data, leading to significant performance overhead.
CROSS: An End-to-End Solution: Developed by Liu Fangxin and Huang Shiyuan from the IMPACT lab, CROSS represents a paradigm shift in sparse computation. It’s an end-to-end compiler framework that tackles the problem from multiple angles:
- Fine-grained analysis: CROSS performsa deep analysis of the sparsity patterns within the input matrices, identifying and exploiting local regularities even within globally irregular structures.
- Adaptive scheduling: Based on the analysis, CROSS dynamically schedules computations, optimizing data access and minimizing redundant operations.
- Optimized kernel generation: The framework generates highly optimized kernelstailored to the specific sparsity patterns, maximizing the utilization of hardware resources.
Performance Gains: The results are striking. CROSS demonstrates significant performance improvements compared to state-of-the-art sparse computation libraries and frameworks, achieving a multifold speedup in AI inference tasks involving unstructured sparse matrices. Thisimprovement translates directly to faster model deployment and reduced computational costs.
Impact and Future Directions: The acceptance of this work at HPCA 2025 underscores its significance to the AI community. CROSS offers a crucial advancement in tackling the efficiency bottleneck of sparse computation, paving the way for faster and more scalableAI applications. Future research will focus on extending CROSS’s capabilities to even more complex sparse patterns and exploring its integration with various hardware platforms.
Conclusion: CROSS provides a significant breakthrough in handling unstructured sparse computations, dramatically improving the efficiency of AI inference. By leveraging fine-grained analysis, adaptive scheduling, andoptimized kernel generation, CROSS overcomes the limitations of existing methods and offers a promising solution for accelerating AI applications. This work highlights the potential of compiler-based optimization techniques in addressing the challenges of modern AI computation.
References:
- [To be added upon publication of the HPCA 2025paper. The reference would include the paper title, authors, conference proceedings, and publication details.]
- [Additional references to relevant works on sparse matrix computation and AI compiler frameworks could be included here.]
Note: This article is written in the style of a professional news piece, aiming for clarity, accuracy, and engagement. The lack of specific quantitative results is due to the absence of such data in the provided source material. Once the HPCA 2025 paper is published, this article can be updated with precise performance figures and a complete reference section.
Views: 0