An infrastructure for scalable and portable parallel programs for computational chemistry

Lotrich, Victor F.; Flocke, N.; Ponton, Mark; Sanders, Beverly A.; Deumens, Erik; Bartlett, Rodney J.; Perera, Ajith

doi:10.1145/1542275.1542361

Cited by 9 publications

(8 citation statements)

References 1 publication

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“…Most of the tools previously developed are specific to their applications and lack transferability. Among those targeting large-scale distributed systems are TCE [4], the ACES/SIAL framework [5], CFOUR [6], which target both distributed and shared-memory architectures, are also quantum chemistry application-specific. Recently, general-purpose tensor tools started to receive more attention, for example CTF [7] and TiledArray [8].…”

Section: Related Workmentioning

confidence: 99%

Cross-scale efficient tensor contractions for coupled cluster computations through multiple programming model backends

Ibrahim

Epifanovsky

Williams

et al. 2017

Journal of Parallel and Distributed Computing

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Coupled-cluster methods provide highly accurate models of molecular structure through explicit numerical calculation of tensors representing the correlation between electrons. These calculations are dominated by a sequence of tensor contractions, motivating the development of numerical libraries for such operations. While based on matrix-matrix multiplication, these libraries are specialized to exploit symmetries in the molecular structure and in electronic interactions, and thus reduce the size of the tensor representation and the complexity of contractions. The resulting algorithms are irregular and their parallelization has been previously achieved via the use of dynamic scheduling or specialized data decompositions. We introduce our efforts to extend the Libtensor framework to work in the distributed memory environment in a scalable and energy-efficient manner. We achieve up to 240× speedup compared with the optimized shared memory implementation of Libtensor. We attain scalability to hundreds of thousands of compute cores on three distributedmemory architectures, (Cray XC30 and XC40, and IBM Blue Gene/Q), and on a heterogeneous GPU-CPU system (Cray XK7). As the bottlenecks shift from being compute-bound DGEMM's to communication-bound collectives as the size of the molecular system scales, we adopt two radically different parallelization approaches for handling load-imbalance, tasking and bulk synchronous models. Nevertheless, we preserve a unified interface to both programming models to maintain the productivity of computational quantum chemists.

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Section: Related Workmentioning

confidence: 99%

Cross-scale efficient tensor contractions for coupled cluster computations through multiple programming model backends

Ibrahim

Epifanovsky

Williams

et al. 2017

Journal of Parallel and Distributed Computing

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“…Most of the tools previously developed are specific to their applications and lack transferability. Among those targeting large-scale distributed systems are TCE [10], the ACES/SIAL framework [15]. CFOUR [17] and MRCC [13] target both distributed and shared-memory architectures, but also remain application-specific.…”

Section: Related Workmentioning

confidence: 99%

Analysis and tuning of libtensor framework on multicore architectures

Ibrahim

Williams

Epifanovsky

et al. 2014

2014 21st International Conference on High Performance Computing (HiPC)

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Abstract-Libtensor is a framework designed to implement the tensor contractions arising form the coupled cluster and equations of motion computational quantum chemistry equations. It has been optimized for symmetry and sparsity to be memory efficient. This allows it to run efficiently on the ubiquitous and cost-effective SMP architectures. Unfortunately, movement of memory controllers on chip has endowed these SMP systems with strong NUMA properties. Moreover, the manycore trend in processor architecture demands that the implementation be extremely thread-scalable on node. To date, Libtensor has been generally agnostic of these effects. To that end, in this paper, we explore a number of optimization techniques including a thread-friendly and NUMA-aware memory allocator and garbage collector, tuning the tensor tiling factor, and tuning the scheduling quanta. In the end, our optimizations can improve the performance of contractions implemented in Libtensor by up to 2× on representative Ivy Bridge, Nehalem, and Opteron SMPs.Index Terms-tensor algebra, parallel programming, quantum chemistry software.

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“…The ACES III package uses the SIAL framework [14], [15] for distributed memory tensor contractions in coupled-cluster theory. Like the NWChem TCE, SIAL uses tiling to extract parallelism from each tensor contraction.…”

Section: B Aces III and Sialmentioning

confidence: 99%

Cyclops Tensor Framework: Reducing Communication and Eliminating Load Imbalance in Massively Parallel Contractions

Solomonik

Matthews

Hammond

et al. 2013

2013 IEEE 27th International Symposium on Parallel and Distributed Processing

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Abstract-Cyclops (cyclic-operations) Tensor Framework (CTF)1 is a distributed library for tensor contractions. CTF aims to scale high-dimensional tensor contractions such as those required in the Coupled Cluster (CC) electronic structure method to massively-parallel supercomputers. The framework preserves tensor structure by subdividing tensors cyclically, producing a regular parallel decomposition. An internal virtualization layer provides completely general mapping support while maintaining ideal load balance. The mapping framework decides on the best mapping for each tensor contraction at run-time via explicit calculations of memory usage and communication volume. CTF employs a general redistribution kernel, which transposes tensors of any dimension between arbitrary distributed layouts, yet touches each piece of data only once. Sequential symmetric contractions are reduced to matrix multiplication calls via tensor index transpositions and partial unpacking. The user-level interface elegantly expresses arbitrary-dimensional generalized tensor contractions in the form of a domain specific language. We demonstrate performance of CC with single and double excitations on BlueGene/Q and Cray XE6 supercomputers.

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An infrastructure for scalable and portable parallel programs for computational chemistry

Cited by 9 publications

References 1 publication

Cross-scale efficient tensor contractions for coupled cluster computations through multiple programming model backends

Cross-scale efficient tensor contractions for coupled cluster computations through multiple programming model backends

Analysis and tuning of libtensor framework on multicore architectures

Cyclops Tensor Framework: Reducing Communication and Eliminating Load Imbalance in Massively Parallel Contractions

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