Batched Sparse Iterative Solvers for Computational Chemistry Simulations on GPUs

Aggarwal, Isha; Kashi, Aditya; Nayak, Pratik; Balos, Cody J.; Woodward, Carol S.; Anzt, Hartwig

doi:10.1109/scala54577.2021.00010

Cited by 8 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, both vectorization and coalesced memory accesses in the SPMV kernel are graph or sparsity pattern dependent. This is the approach followed by Ginkgo [9]. This approach relies on the assumption that the number of non-zero entries is relatively small such that the common data associated to the graph of the sparse matrices can be stored in cache and reused between different solves in the batch.…”

Section: Batched Krylov Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Performance Portable Batched Sparse Linear Solvers

Liegeois

Rajamanickam

Berger-Vergiat

2023

IEEE Trans. Parallel Distrib. Syst.

View full text Add to dashboard Cite

Solving large number of small linear systems is increasingly becoming a bottleneck in computational science applications. While dense linear solvers for such systems have been studied before, batched sparse linear solvers are just starting to emerge. In this paper, we discuss algorithms for solving batched sparse linear systems and their implementation in the Kokkos Kernels library. The new algorithms are performance portable and map well to the hierarchical parallelism available in modern accelerator architectures. The sparse matrix vector product (SPMV) kernel is the main performance bottleneck of the Krylov solvers we implement in this work. The implementation of the batched SPMV and its performance are therefore discussed thoroughly in this paper. The implemented kernels are tested on different Central Processing Unit (CPU) and Graphic Processing Unit (GPU) architectures. We also develop batched Conjugate Gradient (CG) and batched Generalized Minimum Residual (GMRES) solvers using the batched SPMV. Our proposed solver was able to solve 20,000 sparse linear systems on V100 GPUs with a mean speedup of 76x and 924x compared to using a parallel sparse solver with a block diagonal system with all the small linear systems, and compared to solving the small systems one at a time, respectively. We see mean speedup of 0.51 compared to dense batched solver of cuSOLVER on V100, while using lot less memory. Thorough performance evaluation on three different architectures and analysis of the performance are presented.

show abstract

Section: Batched Krylov Methodsmentioning

confidence: 99%

“…• Performance comparisons on CPUs and GPUs that demonstrate that the solver achieves state-of-the-art performance. We are aware of a parallel work to develop batched sparse linear solver [9], [10]. We have developed these methods as part of the same project targeting similar applications.…”

Section: Introductionmentioning

confidence: 99%

Performance Portable Batched Sparse Linear Solvers

Liegeois

Rajamanickam

Berger-Vergiat

2023

IEEE Trans. Parallel Distrib. Syst.

View full text Add to dashboard Cite

show abstract

“…Based on the memory accessor, the Ginkgo team deployed a Compressed Basis GMRES (CB-GMRES) solver that outperforms the standard GMRES solver by storing the Krylov basis vectors in lower precision, therewith accelerating the memory access [192]. Finally, the Ginkgo team successfully employed the batched iterative solvers for the hydrodynamic problems arising in the PeleLM simulations and the gyrokinetic problems arising in the XGC simulations [193].…”

Section: Mixed-precision Methodsmentioning

confidence: 99%

ECP Software Technology Capability Assessment Report V3.0

Heroux

McInnes

et al. 2022

View full text Add to dashboard Cite

The Exascale Computing Project (ECP) Software Technology (ST) focus area is responsible for (1) developing critical software capabilities that will enable the successful execution of ECP applications and (2) providing key components of a productive and sustainable exascale computing ecosystem that will position the US Department of Energy (DOE) and the broader high-performance computing (HPC) community with a firm foundation for future extreme-scale computing capabilities.This ECP ST Capability Assessment Report (CAR) provides an overview and assessment of current ECP ST capabilities and activities, giving stakeholders and the broader HPC community information that can be used to assess ECP ST progress and plan their own efforts accordingly. ECP ST leaders commit to updating this document on regular basis (every 6-12 months). Highlights from this version of the report are presented here.This version of the CAR contains the following updates relative to the previous revision.• This report highlights the progress with the Extreme-scale Scientific Software Stack (E4S) efforts.In particular, this report discusses how E4S continues to gain traction as a first-class entity in the HPC ecosystem, enabling new conversations with users, facilities, vendors, other US agencies, and international partners.• The several-page summaries of each ECP Level 4 project were updated to reflect recent progress and next steps (Section 4). Of particular note are the experiences of our teams on early-access systems for Frontier.• The E4S is described further. E4S is now updated via quarterly releases. E4S is the primary integration and delivery vehicle for ECP ST capabilities (Section 2.1.1).• The ECP ST software development kit (SDK) effort further refined its groupings (Section 2.1.2).The ECP ST focus area represents the key bridge between exascale systems and the scientists developing applications that will run on those platforms. ECP ST efforts contribute to approximately 70 software products (Section 2.1.3) in six technical areas (Table 1). Since publishing the previous revision of the CAR, the team has continued to evolve the product dictionary of official product names, which enables more rigorous mapping of ECP ST deliverables to stakeholders (Section 2.1.4).Programming Models & Runtimes: In addition to developing key enhancements to MPI and OpenMP for scalable systems with accelerated node architectures, the team is working on performance portability layers (Kokkos and RAJA) and participating in OpenMP and OpenACC software design and development that will enable applications to write much of their source code without needing to provide vendor-specific implementations for each exascale system. One legacy of ECP ST efforts is anticipated to be a software stack that supports Intel and AMD accelerators in addition to NVIDIA's accelerators (Section 4.1).Development Tools: The team is enhancing existing widely used compilers (e.g., LLVM) and performance tools for next-generation platforms. Compilers are critical for heterogeneous archi...

show abstract