A Systematic Survey of General Sparse Matrix-Matrix Multiplication

Gao, Jianhua; Ji, Weixing; Tan, Zhaonian; Zhao, Yueyan

doi:10.48550/arxiv.2002.11273

Cited by 4 publications

(3 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Contrary to quantum states, however, quantum gates utilise scipy's Compressed Sparsed Row matrix implementation. This format will of course, be improved upon to not only facilitate SpMV and GeMV but also SpGeMM or Sparse Matrix-Matrix multiplication and GeMM, or General Matrix-Matrix multiplication [35]. However, we have added support for numpy matrices for small circuits.…”

Section: Representation Of Quantum Gatesmentioning

confidence: 99%

QuDiet: A classical simulation platform for qubit‐qudit hybrid quantum systems

Chatterjee

Das

Bala

et al. 2023

IET Quantum Communication

View full text Add to dashboard Cite

In recent years, numerous research advancements have extended the limit of classical simulation of quantum algorithms. Although, most of the state‐of‐the‐art classical simulators are only limited to binary quantum systems, which restrict the classical simulation of higher‐dimensional quantum computing systems. Through recent developments in higher‐dimensional quantum computing systems, it is realised that implementing qudits improves the overall performance of a quantum algorithm by increasing memory space and reducing the asymptotic complexity of a quantum circuit. Hence, in this article, QuDiet, a state‐of‐the‐art user‐friendly python‐based higher‐dimensional quantum computing simulator is introduced. QuDiet offers multi‐valued logic operations by utilising generalised quantum gates with an abstraction so that any naive user can simulate qudit systems with ease as compared to the existing ones. Various benchmark quantum circuits is simulated in QuDiet and show the considerable speedup in simulation time as compared to the other simulators without loss in precision. Finally, QuDiet provides a full qubit‐qudit hybrid quantum simulator package with quantum circuit templates of well‐known quantum algorithms for fast prototyping and simulation. Comprehensive simulation up to 20 qutrits circuit on depth 80 on QuDiet was successfully achieved. The complete code and packages of QuDiet is available at https://github.com/LegacYFTw/QuDiet.

show abstract

Section: Representation Of Quantum Gatesmentioning

confidence: 99%

QuDiet: A classical simulation platform for qubit‐qudit hybrid quantum systems

Chatterjee

Das

Bala

et al. 2023

IET Quantum Communication

View full text Add to dashboard Cite

show abstract

“…Essentially, accelerating SNN inference with our algorithms is the SpGEMM problem, as explained in Section 2.2. SpGEMM is very popular in high-performance computing, mainly used in algebra and graph analysis (Gao et al, 2020 ). The vast majority of the relevant studies, such as Davis ( 2018 ), Zhang et al ( 2020 ), and An and Çatalyürek ( 2021 ), are based on the “row-wise” algorithm proposed by Gustavson ( 1978 ), also known as compressed sparse row format (CSR) or Yale sparse matrix format.…”

Section: Introductionmentioning

confidence: 99%

Efficient SNN multi-cores MAC array acceleration on SpiNNaker 2

et al. 2023

View full text Add to dashboard Cite

The potential low-energy feature of the spiking neural network (SNN) engages the attention of the AI community. Only CPU-involved SNN processing inevitably results in an inherently long temporal span in the cases of large models and massive datasets. This study introduces the MAC array, a parallel architecture on each processing element (PE) of SpiNNaker 2, into the computational process of SNN inference. Based on the work of single-core optimization algorithms, we investigate the parallel acceleration algorithms for collaborating with multi-core MAC arrays. The proposed Echelon Reorder model information densification algorithm, along with the adapted multi-core two-stage splitting and authorization deployment strategies, achieves efficient spatio-temporal load balancing and optimization performance. We evaluate the performance by benchmarking a wide range of constructed SNN models to research on the influence degree of different factors. We also benchmark with two actual SNN models (the gesture recognition model of the real-world application and balanced random cortex-like network from neuroscience) on the neuromorphic multi-core hardware SpiNNaker 2. The echelon optimization algorithm with mixed processors realizes 74.28% and 85.78% memory footprint of the original MAC calculation on these two models, respectively. The execution time of echelon algorithms using only MAC or mixed processors accounts for ≤ 24.56% of the serial ARM baseline. Accelerating SNN inference with algorithms in this study is essentially the general sparse matrix-matrix multiplication (SpGEMM) problem. This article explicitly expands the application field of the SpGEMM issue to SNN, developing novel SpGEMM optimization algorithms fitting the SNN feature and MAC array.

show abstract

“…Motivation and significance. Computing the product of two matrices is one of the most fundamental operations in scientific computing [13]. Matrix-matrix multiplication is a core operation in for example combinatorics [10], deep learning [9], and electronic structure theory [7,16].…”

mentioning

confidence: 99%

The Chunks and Tasks Matrix Library 2.0

Rubensson¹,

Rudberg²,

Kruchinina³

et al. 2020

Preprint

View full text Add to dashboard Cite

We present a C++ header-only parallel sparse matrix library, based on sparse quadtree representation of matrices using the Chunks and Tasks programming model. The library implements a number of sparse matrix algorithms for distributed memory parallelization that are able to dynamically exploit data locality to avoid movement of data. This is demonstrated for the example of block-sparse matrix-matrix multiplication applied to three sequences of matrices with different nonzero structure, using the CHT-MPI 2.0 runtime library implementation of the Chunks and Tasks model. The runtime library succeeds to dynamically load balance the calculation regardless of the sparsity structure.

show abstract

A Systematic Survey of General Sparse Matrix-Matrix Multiplication

Cited by 4 publications

References 76 publications

QuDiet: A classical simulation platform for qubit‐qudit hybrid quantum systems

QuDiet: A classical simulation platform for qubit‐qudit hybrid quantum systems

Efficient SNN multi-cores MAC array acceleration on SpiNNaker 2

The Chunks and Tasks Matrix Library 2.0

Contact Info

Product

Resources

About