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AcceleratedLiNGAM: Learning Causal DAGs at the speed of GPUs (2403.03772v1)

Published 6 Mar 2024 in cs.LG, cs.DC, and stat.ML

Abstract: Existing causal discovery methods based on combinatorial optimization or search are slow, prohibiting their application on large-scale datasets. In response, more recent methods attempt to address this limitation by formulating causal discovery as structure learning with continuous optimization but such approaches thus far provide no statistical guarantees. In this paper, we show that by efficiently parallelizing existing causal discovery methods, we can in fact scale them to thousands of dimensions, making them practical for substantially larger-scale problems. In particular, we parallelize the LiNGAM method, which is quadratic in the number of variables, obtaining up to a 32-fold speed-up on benchmark datasets when compared with existing sequential implementations. Specifically, we focus on the causal ordering subprocedure in DirectLiNGAM and implement GPU kernels to accelerate it. This allows us to apply DirectLiNGAM to causal inference on large-scale gene expression data with genetic interventions yielding competitive results compared with specialized continuous optimization methods, and Var-LiNGAM for causal discovery on U.S. stock data.

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References (30)
  1. A causal inference approach for estimating effects of non-pharmaceutical interventions during covid-19 pandemic. Plos one, 17(9):e0265289, 2022.
  2. Inferring causal relationships between risk factors and outcomes from genome-wide association study data. Annual review of genomics and human genetics, 19:303–327, 2018.
  3. Flashattention: Fast and memory-efficient exact attention with io-awareness. Advances in Neural Information Processing Systems, 35:16344–16359, 2022.
  4. Perturb-seq: dissecting molecular circuits with scalable single-cell rna profiling of pooled genetic screens. cell, 167(7):1853–1866, 2016.
  5. Multimodal pooled perturb-cite-seq screens in patient models define mechanisms of cancer immune evasion. Nature genetics, 53(3):332–341, 2021.
  6. Using bayesian networks to analyze expression data. In Proceedings of the fourth annual international conference on Computational molecular biology, pages 127–135, 2000.
  7. Flashfftconv: Efficient convolutions for long sequences with tensor cores. arXiv preprint arXiv:2311.05908, 2023.
  8. Review of causal discovery methods based on graphical models. Frontiers in genetics, 10:524, 2019.
  9. John L Gustafson. Reevaluating amdahl’s law. Communications of the ACM, 31(5):532–533, 1988.
  10. Pairwise likelihood ratios for estimation of non-gaussian structural equation models. The Journal of Machine Learning Research, 14(1):111–152, 2013.
  11. Estimation of a structural vector autoregression model using non-gaussianity. Journal of Machine Learning Research, 11(5), 2010.
  12. Causal inference in statistics, social, and biomedical sciences. Cambridge University Press, 2015.
  13. Algorithmic bias in recidivism prediction: A causal perspective. In 34th AAAI Conference on Artificial Intelligence, AAAI 2020, pages 13839–13840. AAAI press, 2020.
  14. Scaling structural learning with no-bears to infer causal transcriptome networks. In PACIFIC SYMPOSIUM ON BIOCOMPUTING 2020, pages 391–402. World Scientific, 2019.
  15. Stein variational gradient descent: A general purpose bayesian inference algorithm. Advances in neural information processing systems, 29, 2016.
  16. The impact of incarceration on recidivism. Annual Review of Criminology, 5:133–152, 2022.
  17. Large-scale differentiable causal discovery of factor graphs. In Advances in Neural Information Processing Systems, 2022.
  18. Accelerating lingam causal discovery with massive parallel execution on supercomputer fugaku. IEICE TRANSACTIONS on Information and Systems, 105(12):2032–2039, 2022.
  19. Scalable causal discovery with score matching. arXiv preprint arXiv:2304.03382, 2023.
  20. On the role of sparsity and dag constraints for learning linear dags. Advances in Neural Information Processing Systems, 33:17943–17954, 2020.
  21. Structure learning with continuous optimization: A sober look and beyond. arXiv preprint arXiv:2304.02146, 2023.
  22. Inferring subnetworks from perturbed expression profiles. BIOINFORMATICS-OXFORD-, 17:S215–S224, 2001.
  23. Judea Pearl et al. Models, reasoning and inference. Cambridge, UK: CambridgeUniversityPress, 19(2):3, 2000.
  24. The causation of disease–the practical and ethical consequences of competing explanations. Medicine, Health Care and Philosophy, 9:293–306, 2006.
  25. Beware of the simulated dag! causal discovery benchmarks may be easy to game. Advances in Neural Information Processing Systems, 34:27772–27784, 2021.
  26. Paralingam: Parallel causal structure learning for linear non-gaussian acyclic models. Journal of Parallel and Distributed Computing, 176:114–127, 2023.
  27. A linear non-gaussian acyclic model for causal discovery. Journal of Machine Learning Research, 7(10), 2006.
  28. Directlingam: A direct method for learning a linear non-gaussian structural equation model. Journal of Machine Learning Research-JMLR, 12(Apr):1225–1248, 2011.
  29. On the identifiability of the post-nonlinear causal model. arXiv preprint arXiv:1205.2599, 2012.
  30. Dags with no tears: Continuous optimization for structure learning. Advances in neural information processing systems, 31, 2018.

Summary

  • The paper introduces AcceleratedLiNGAM, a GPU-accelerated method that achieves up to a 32-fold speed-up in causal discovery without sacrificing accuracy.
  • The method optimizes the causal ordering process by parallelizing computationally intense operations normally requiring quadratic or cubic time.
  • Empirical results validate its performance on diverse datasets, enabling advanced causal inference in genomics and finance.

Accelerating Causal Discovery with GPUs: An Analysis of LiNGAM Methods on Large-Scale Data

Introduction to AcceleratedLiNGAM

Causal discovery techniques play a pivotal role in various scientific fields by enabling the identification of causal relationships from observational data. Among the renowned causal discovery methods, the LiNGAM (Linear Non-Gaussian Acyclic Model) family stands out due to its unique utilization of non-Gaussian data properties to discern causal relationships within a directed acyclic graph (DAG) framework. However, the computational complexity of existing LiNGAM implementations, primarily due to their reliance on quadratic or cubic operations relative to the number of variables, severely limits their applicability to large datasets. This paper introduces AcceleratedLiNGAM, an optimized implementation leveraging GPU processing capabilities to significantly enhance computational efficiency, thus enabling the application of LiNGAM methodologies to large-scale problems.

GPU Parallelization: A Path to Efficiency

The cornerstone of accelerating LiNGAM on GPUs is the parallelization of costly computational patterns, particularly the causal ordering discovery procedure, which is the most time-consuming aspect of DirectLiNGAM and VarLiNGAM analyses. Through careful allocation of computational tasks across GPU threads, and optimizing memory usage and operations such as parallel reductions within GPU memory hierarchies, the AcceleratedLiNGAM approach realizes a substantial reduction in execution time. Notably, this optimization achieves up to a 32-fold speed-up compared to conventional CPU-based executions without compromising the statistical properties and guarantees inherent to the LiNGAM methodology.

Implementation and Validation

The actualization of AcceleratedLiNGAM is validated on a NVIDIA RTX 6000 Ada GPU, with implementation details meticulously crafted to balance memory usage and computational efficiency. This balanced approach ensures that while the vast parallel processing power of GPUs is harnessed, sensitivities such as non-associative floating-point operation errors and synchronization overheads are adequately addressed. Empirical validation on simulated datasets confirms that AcceleratedLiNGAM faithfully reproduces results consistent with traditional LiNGAM implementations, thereby validating its accuracy and reliability.

Applications and Impact

AcceleratedLiNGAM's application to large-scale gene expression data with genetic interventions showcases its potential to facilitate advanced causal inference in genomics, a domain where understanding genetic interactions can lead to breakthroughs in medicine and biology. Similarly, applying AcceleratedLiNGAM to financial data, such as stock indices, provides insights into causal relationships within complex economic systems, highlighting AcceleratedLiNGAM's versatility and its potential to contribute meaningfully across varied domains where causal discovery is crucial.

Future Directions and Community Engagement

While AcceleratedLiNGAM marks a significant advancement in the scalable application of causal discovery methodologies, it also opens avenues for further optimizations and applications. Future work could explore deeper GPU-specific optimizations, incorporation of emerging GPU technologies, and expansions to accommodate a broader array of causal discovery models. Recognizing the broader community's role in this evolutionary process, the decision to open-source AcceleratedLiNGAM seeks to foster collaborative enhancements and applications, inviting contributions and explorations from varied fields.

Concluding Remarks

In summary, AcceleratedLiNGAM represents a significant leap forward in enabling the application of LiNGAM causal discovery techniques to large-scale datasets. By leveraging GPU technologies, it overcomes computational barriers and opens new horizons for causal analysis across scientific domains. This work not only contributes a practical tool for researchers but also sets the stage for future innovations in the field of causal discovery.

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