Effective Sparse Matrix Representation For The GPU Architectures

Neelima, B.

doi:10.5121/ijcsea.2012.2213

Cited by 12 publications

(4 citation statements)

References 20 publications

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“…The rationale of choosing wavelet basis instead of original Fourier basis is as follows. First of all, the Wavelet basis is much sparser than the Fourier basis and most suitable for modern GPU architectures for efficient training [24]. Moreover, by the nature of wavelet basis, the efficient polynomial approximation can be achieved more easily.…”

Section: Hwnn: Hypergraph Wavelet Neural Networkmentioning

confidence: 99%

Heterogeneous Hypergraph Embedding for Graph Classification

Sun

Yin

Liu

et al. 2020

Preprint

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Recently, graph neural networks have been widely used for network embedding because of their prominent performance in pairwise relationship learning. In the real world, a more natural and common situation is the coexistence of pairwise relationships and complex non-pairwise relationships, which is, however, rarely studied. In light of this, we propose a graph neural network-based representation learning framework for heterogeneous hypergraphs, an extension of conventional graphs, which can well characterize multiple non-pairwise relations. Our framework first projects the heterogeneous hypergraph into a series of snapshots and then we take the Wavelet basis to perform localized hypergraph convolution. Since the Wavelet basis is usually much sparser than the Fourier basis, we develop an efficient polynomial approximation to the basis to replace the time-consuming Laplacian decomposition. Extensive evaluations have been conducted and the experimental results show the superiority of our method. In addition to the standard tasks of network embedding evaluation such as node classification, we also apply our method to the task of spammers detection and the superior performance of our framework shows that relationships beyond pairwise are also advantageous in the spammer detection.To make our experiment repeatable, source codes and related datasets are available at https://xiangguosun.mystrikingly.com. CCS CONCEPTS• Computing methodologies → Neural networks.

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Section: Hwnn: Hypergraph Wavelet Neural Networkmentioning

confidence: 99%

Heterogeneous Hypergraph Embedding for Graph Classification

Sun

Yin

Liu

et al. 2020

Preprint

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“…3). We use a sparse matrix representation [Neelima and Raghavendra 2012] to store the decompressed reference images in the memory while rendering. The sparse matrix representation reduces the additional run-time memory overhead required for the motion re-compensation step to decode a given block of pixels.…”

Section: Decompression Memory Overheadmentioning

confidence: 99%

HMLFC: Hierarchical Motion‐Compensated Light Field Compression for Interactive Rendering

Pratapa

Manocha

2019

Computer Graphics Forum

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We present a new motion‐compensated hierarchical compression scheme (HMLFC) for encoding light field images (LFI) that is suitable for interactive rendering. Our method combines two different approaches, motion compensation schemes and hierarchical compression methods, to exploit redundancies in LFI. The motion compensation schemes capture the redundancies in local regions of the LFI efficiently (local coherence) and the hierarchical schemes capture the redundancies present across the entire LFI (global coherence). Our hybrid approach combines the two schemes effectively capturing both local as well as global coherence to improve the overall compression rate. We compute a tree from LFI using a hierarchical scheme and use phase shifted motion compensation techniques at each level of the hierarchy. Our representation provides random access to the pixel values of the light field, which makes it suitable for interactive rendering applications using a small run‐time memory footprint. Our approach is GPU friendly and allows parallel decoding of LF pixel values. We highlight the performance on the two‐plane parameterized light fields and obtain a compression ratio of 30–800× with a PSNR of 40–45 dB. Overall, we observe a ∼2–5× improvement in compression rates using HMLFC over prior light field compression schemes that provide random access capability. In practice, our algorithm can render new views of resolution 512 × 512 on an NVIDIA GTX‐980 at ∼200 fps.

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“…Another flat format named ELLR-T is proposed by Dziekonski et al [2] for optimizing SpMV computation on GPU. Bayyapu et al [3]- [5] proposed Bit Level Single Indexing (BLSI) format to optimize pre-GPU computation overheads while computing SpMV on GPU.…”

Section: Related Workmentioning

confidence: 99%

A GPU Framework for Sparse Matrix Vector Multiplication

Neelima

Reddy

Raghavendra

2014

2014 IEEE 13th International Symposium on Parallel and Distributed Computing

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The hardware and software evolutions related to Graphics Processing Units (GPUs), for general purpose computations, have changed the way the parallel programming issues are addressed. Many applications are being ported onto GPU for achieving performance gain. The GPU execution time is continuously optimized by the GPU programmers while optimizing pre-GPU computation overheads attracted the research community in the recent past. While GPU executes the programs given by a CPU, pre-GPU computation overheads does exists and should be optimized for a better usage of GPUs. The GPU framework proposed in this paper improves the overall performance of the application by optimizing pre-GPU computation overheads along with GPU execution time. This paper proposes a sparse matrix format prediction tool to predict an optimal sparse matrix format to be used for a given input matrix by analyzing the input sparse matrix and considering pre-GPU computation overheads. The sparse matrix format predicted by the proposed method is compared against the best performing sparse matrix formats posted in the literature. The proposed model is based on the static data that is available from the input directly and hence the prediction overhead is very small. Compared to GPU specific sparse format prediction, the proposed model is more inclusive and precious in terms of increasing overall application's performance.

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Effective Sparse Matrix Representation For The GPU Architectures

Cited by 12 publications

References 20 publications

Heterogeneous Hypergraph Embedding for Graph Classification

Heterogeneous Hypergraph Embedding for Graph Classification

HMLFC: Hierarchical Motion‐Compensated Light Field Compression for Interactive Rendering

A GPU Framework for Sparse Matrix Vector Multiplication

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