Abstract
The computational power increases over the past decades have greatly enhanced the ability to simulate chemical reactions and understand ever more complex transformations. Tensor contractions are the fundamental computational building block of these simulations. These simulations have often been tied to one platform and restricted in generality by the interface provided to the user. The expanding prevalence of accelerators and researcher demands necessitate a more general approach which is not tied to specific hardware or requires contortion of algorithms to specific hardware platforms. In this paper we present COMET, a domain-specific programming language and compiler infrastructure for tensor contractions targeting heterogeneous accelerators. We present a system of progressive lowering through multiple layers of abstraction and optimization that achieves up to \(1.98\times \) speedup for 30 tensor contractions commonly used in computational chemistry and beyond.
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References
Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). https://www.tensorflow.org/. software available from tensorflow.org
Aprá, E., et al.: NWChem: past, present, and future. J. Chem. Phys. 152(18), 184102 (2020). https://doi.org/10.1063/5.0004997
Einstein, A.: Die grundlage der allgemeinen relativitätstheorie. Ann. Phys. 354(7), 769–822 (1916). https://doi.org/10.1002/andp.19163540702
Epifanovsky, E., et al.: New implementation of high-level correlated methods using a general block tensor library for high-performance electronic structure calculations. J. Comput. Chem. 34(26), 2293–2309 (2013)
Fishman, M., White, S.R., Stoudenmire, E.M.: The itensor software library for tensor network calculations (2020)
Google: TensorNetwork (2020). https://github.com/google/TensorNetwork
Gunnels, J.A., et al.: FLAME: formal linear algebra methods environment. ACM Trans. Math. Softw. 27(4), 422–455 (2001)
Hirata, S.: Tensor contraction engine: abstraction and automated parallel implementation of configuration-interaction, coupled-cluster, and many-body perturbation theories. J. Phys. Chem. A 107(46), 9887–9897 (2003)
Intel: Math kernel library (2012). http://developer.intel.com/software/products/mkl/
Jouppi, N.P., et al.: In-datacenter performance analysis of a tensor processing unit. In: Proceedings of the 44th Annual International Symposium on Computer Architecture, pp. 1–12 (2017)
Lattner, C., Adve, V.: LLVM: a compilation framework for lifelong program analysis and transformation. In: CGO ’04, San Jose, CA, USA, pp. 75–88 (2004)
Lattner, C., et al.: MLIR: a compiler infrastructure for the end of Moore’s law. arXiv preprint arXiv:2002.11054 (2020)
Poulson, J., et al.: Elemental: a new framework for distributed memory dense matrix computations. ACM Trans. Math. Softw. 39(2), 13:1–13:24 (2013)
Ragan-Kelley, J., et al.: Halide: A language and compiler for optimizing parallelism, locality, and recomputation in image processing pipelines. In: PLDI’13, pp. 519–530 (2013)
Raghavachari, K., et al.: A fifth-order perturbation comparison of electron correlation theories. Chem. Phys. Lett. 157, 479–483 (1989)
Schatz, M., van de Geijn, R., Poulson, J.: Parallel matrix multiplication: a systematic journey. SIAM J. Sci. Comput. 38(6), C748–C781 (2016)
Schatz, M.D., Low, T.M., van de Geijn, R.A., Kolda, T.G.: Exploiting symmetry in tensors for high performance: multiplication with symmetric tensors. SIAM J. Sci. Comput. 36(5), C453–C479 (2014)
Shao, Y.S., et al.: Aladdin: a pre-RTL, power-performance accelerator simulator enabling large design space exploration of customized architectures. In: ISCA, pp. 97–108. IEEE Press (2014)
Solomonik, E., Matthews, D., Hammond, J.R., Stanton, J.F., Demmel, J.: A massively parallel tensor contraction framework for coupled-cluster computations. J. Parallel Distrib. Comput. 74(12), 3176–3190 (2014)
Springer, P., Bientinesi, P.: Design of a high-performance Gemm-like tensor-tensor multiplication. ACM Trans. Math. Softw. 44(3), 1–29 (2018)
Springer, P., Su, T., Bientinesi, P.: HPTT: a high-performance tensor transposition C++ library, pp. 56–62 (2017)
Van Zee, F.G., Van De Geijn, R.A.: BLIS: a framework for rapidly instantiating BLAS functionality. ACM Trans. Math. Softw. 41(3), 14:1–14:33 (2015)
Acknowledgement
This research is supported by PNNL Laboratory Directed Research and Development Program (LDRD), Data-Model Convergence Initiative, project DuoMO: A Compiler Infrastructure for Data-Model Convergence, and project Hybrid Advanced Workflows.
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Mutlu, E. et al. (2022). COMET: A Domain-Specific Compilation of High-Performance Computational Chemistry. In: Chapman, B., Moreira, J. (eds) Languages and Compilers for Parallel Computing. LCPC 2020. Lecture Notes in Computer Science(), vol 13149. Springer, Cham. https://doi.org/10.1007/978-3-030-95953-1_7
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DOI: https://doi.org/10.1007/978-3-030-95953-1_7
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