面向OpenCL架构的GPGPU量化性能模型

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Abstract

为了评估数据并行(DLP)应用并行化后在GPU体系结构上的执行性能,针对OpenCL架构提出一种GPGPU量化性能模型.该模型充分考虑了影响GPGPU程序性能的各种因素:全局存储器访问、局部存储器访问、计算与访存重叠、条件分支转移和同步.通过对DLP应用的静态分析并设定具体的OpenCL执行配置,在无需编写实际GPGPU程序的前提下采用该模型即可估算出DLP应用在GPU体系结构上的执行时间.在AMD RadeonTMHD 5870 GPU和NVIDIA GeForceTMGTX 280 GPU上对矩阵乘法与并行前缀和的分析与实验结果表明:该性能模型能够相对准确地评估DLP应用并行化后的执行时间.

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Key words

data-level parallel,GPGPU,performance model,GPU,OpenCL

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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