At-Scale Sparse Deep Neural Network Inference with Efficient GPU Implementation

• URL: http://arxiv.org/abs/2007.14152v2
• Date: Wed, 2 Sep 2020 23:30:02 GMT
• Title: At-Scale Sparse Deep Neural Network Inference with Efficient GPU Implementation
• Authors: Mert Hidayetoglu, Carl Pearson, Vikram Sharma Mailthody, Eiman Ebrahimi, Jinjun Xiong, Rakesh Nagi, Wen-Mei Hwu
• Abstract summary: This paper presents GPU performance optimization and scaling results for inference models of the Sparse Deep Neural Network Challenge 2020. Sparse deep neural networks (SpDNN) have shown promise for reining in the memory footprint of large neural networks. This work presents optimized sparse matrix multiplication kernels fused with the ReLU function.
• Score: 24.824295164938604
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents GPU performance optimization and scaling results for inference models of the Sparse Deep Neural Network Challenge 2020. Demands for network quality have increased rapidly, pushing the size and thus the memory requirements of many neural networks beyond the capacity of available accelerators. Sparse deep neural networks (SpDNN) have shown promise for reining in the memory footprint of large neural networks. However, there is room for improvement in implementing SpDNN operations on GPUs. This work presents optimized sparse matrix multiplication kernels fused with the ReLU function. The optimized kernels reuse input feature maps from the shared memory and sparse weights from registers. For multi-GPU parallelism, our SpDNN implementation duplicates weights and statically partition the feature maps across GPUs. Results for the challenge benchmarks show that the proposed kernel design and multi-GPU parallelization achieve up to 180 tera-edges per second inference throughput. These results are up to 4.3x faster for a single GPU and an order of magnitude faster at full scale than those of the champion of the 2019 Sparse Deep Neural Network Graph Challenge for the same generation of NVIDIA V100 GPUs. Using the same implementation, we also show single-GPU throughput on NVIDIA A100 is 2.37$\times$ faster than V100.

Related papers

• Fully-fused Multi-Layer Perceptrons on Intel Data Center GPUs [3.7101665559244874]
  This paper presents a SYCL implementation of Multi-formedLayer Perceptrons (MLPs) for the Intel Data Center GPU Max 1550. We show with a simple model that this results in a significant increase in arithmetic intensity, leading to improved performance, especially for inference.
  arXiv  Detail & Related papers  (2024-03-26T11:38:39Z)
• Instant Neural Graphics Primitives with a Multiresolution Hash Encoding [67.33850633281803]
  We present a versatile new input encoding that permits the use of a smaller network without sacrificing quality. A small neural network is augmented by a multiresolution hash table of trainable feature vectors whose values are optimized through a gradient descent. We achieve a combined speed of several orders of magnitude, enabling training of high-quality neural graphics primitives in a matter of seconds.
  arXiv  Detail & Related papers  (2022-01-16T07:22:47Z)
• AxoNN: An asynchronous, message-driven parallel framework for extreme-scale deep learning [1.5301777464637454]
  AxoNN is a parallel deep learning framework that exploits asynchrony and message-driven execution to schedule neural network operations on each GPU. By using the CPU memory as a scratch space for offloading data periodically during training, AxoNN is able to reduce GPU memory consumption by four times.
  arXiv  Detail & Related papers  (2021-10-25T14:43:36Z)
• Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining [58.10436813430554]
  Mini-batch training of graph neural networks (GNNs) requires a lot of computation and data movement. We argue in favor of performing mini-batch training with neighborhood sampling in a distributed multi-GPU environment. We present a sequence of improvements to mitigate these bottlenecks, including a performance-engineered neighborhood sampler. We also conduct an empirical analysis that supports the use of sampling for inference, showing that test accuracies are not materially compromised.
  arXiv  Detail & Related papers  (2021-10-16T02:41:35Z)
• VersaGNN: a Versatile accelerator for Graph neural networks [81.1667080640009]
  We propose textitVersaGNN, an ultra-efficient, systolic-array-based versatile hardware accelerator. textitVersaGNN achieves on average 3712$times$ speedup with 1301.25$times$ energy reduction on CPU, and 35.4$times$ speedup with 17.66$times$ energy reduction on GPU.
  arXiv  Detail & Related papers  (2021-05-04T04:10:48Z)
• Efficient and Generic 1D Dilated Convolution Layer for Deep Learning [52.899995651639436]
  We introduce our efficient implementation of a generic 1D convolution layer covering a wide range of parameters. It is optimized for x86 CPU architectures, in particular, for architectures containing Intel AVX-512 and AVX-512 BFloat16 instructions. We demonstrate the performance of our optimized 1D convolution layer by utilizing it in the end-to-end neural network training with real genomics datasets.
  arXiv  Detail & Related papers  (2021-04-16T09:54:30Z)
• DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks [58.48833325238537]
  Full-batch training on Graph Neural Networks (GNN) to learn the structure of large graphs is a critical problem that needs to scale to hundreds of compute nodes to be feasible. In this paper, we presentGNN that optimize the well-known Deep Graph Library (DGL) for full-batch training on CPU clusters. Our results on four common GNN benchmark datasets show up to 3.7x speed-up using a single CPU socket and up to 97x speed-up using 128 CPU sockets.
  arXiv  Detail & Related papers  (2021-04-14T08:46:35Z)
• Learning N:M Fine-grained Structured Sparse Neural Networks From Scratch [75.69506249886622]
  Sparsity in Deep Neural Networks (DNNs) has been widely studied to compress and accelerate the models on resource-constrained environments. In this paper, we are the first to study training from scratch an N:M fine-grained structured sparse network.
  arXiv  Detail & Related papers  (2021-02-08T05:55:47Z)
• Optimization of XNOR Convolution for Binary Convolutional Neural Networks on GPU [2.578242050187029]
  We propose an implementation of binary convolutional network inference on GPU. Experimental results show that using GPU can provide a speed-up of up to $42.61times$ with a kernel size of $3times3$.
  arXiv  Detail & Related papers  (2020-07-28T13:01:17Z)
• TFApprox: Towards a Fast Emulation of DNN Approximate Hardware Accelerators on GPU [0.4817429789586127]
  Energy efficiency of hardware accelerators of deep neural networks (DNN) can be improved by introducing approximate arithmetic circuits. A software emulation of the DNN accelerator is usually executed on CPU or GPU. This emulation is typically two or three orders of magnitude slower than a software DNN implementation running on or emulated.
  arXiv  Detail & Related papers  (2020-02-21T08:22:56Z)
• Performance Aware Convolutional Neural Network Channel Pruning for Embedded GPUs [6.035819238203187]
  We show that a reduction in the number of convolutional channels, pruning 12% of the initial size, is in some cases detrimental to performance. We also find examples where performance-aware pruning achieves the intended results, with performance speedups of 3x with cuDNN and above 10x with Arm Compute Library and TVM.
  arXiv  Detail & Related papers  (2020-02-20T12:07:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.