Indar Sugiarto, D. Prayogo, H. Palit, Felix Pasila, Resmana Lim, A. Noertjahyana, I. G. Widyadana, S. Hermawan, Agustinus Bimo Gumelar, B. Yahya
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The method for constructing PakCarik was based on a computer hardware assembling technique that uses commercial off-the-shelf hardware and was tested on several AI-related application scenarios. The testing methods in this experiment include: high-performance lapack (HPL) benchmarking, message passing interface (MPI) benchmarking, and TensorFlow (TF) benchmarking. From the experiment, the authors can observe that PakCarik's performance is quite similar to the commonly used cloud computing services such as Google Compute Engine and Amazon EC2, even though falls a bit behind the dedicated AI platform such as Nvidia DGX-1 used in the benchmarking experiment. Its maximum computing performance was measured at 326 Gflops. 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引用次数: 1
摘要
本文描述了一个专门用于人工智能探索的计算平台的原型。该平台被称为PakCarik,本质上是一个具有GPU(图形处理单元)加速功能的高通量计算平台。PakCarik是Platform Komputasi Cerdas Ramah Industri Kreatif的印尼语首字母缩写,可翻译为“创意产业友好型智能计算平台”。该平台旨在为基于人工智能的项目,特别是那些依赖机器学习和多目标优化范式的项目,提供完整的开发和生产环境。构建PakCarik的方法基于一种使用商用现成硬件的计算机硬件组装技术,并在几个与人工智能相关的应用场景中进行了测试。本实验中的测试方法包括:高性能lapack(HPL)基准测试、消息传递接口(MPI)基准测试和TensorFlow(TF)基准测试。从实验中,作者可以观察到,PakCarik的性能与谷歌计算引擎和亚马逊EC2等常用的云计算服务非常相似,尽管有点落后于基准实验中使用的英伟达DGX-1等专用人工智能平台。其最大计算性能为326 Gflops。作者得出的结论是,PakCarik已经准备好部署在现实世界的应用程序中,并且可以通过在其中添加更多的GPU卡来使其更加强大。
Custom Built of Smart Computing Platform for Supporting Optimization Methods and Artificial Intelligence Research
This paper describes a prototype of a computing platform dedicated to artificial intelligence explorations. The platform, dubbed as PakCarik, is essentially a high throughput computing platform with GPU (graphics processing units) acceleration. PakCarik is an Indonesian acronym for Platform Komputasi Cerdas Ramah Industri Kreatif, which can be translated as “Creative Industry friendly Intelligence Computing Platform”. This platform aims to provide complete development and production environment for AI-based projects, especially to those that rely on machine learning and multiobjective optimization paradigms. The method for constructing PakCarik was based on a computer hardware assembling technique that uses commercial off-the-shelf hardware and was tested on several AI-related application scenarios. The testing methods in this experiment include: high-performance lapack (HPL) benchmarking, message passing interface (MPI) benchmarking, and TensorFlow (TF) benchmarking. From the experiment, the authors can observe that PakCarik's performance is quite similar to the commonly used cloud computing services such as Google Compute Engine and Amazon EC2, even though falls a bit behind the dedicated AI platform such as Nvidia DGX-1 used in the benchmarking experiment. Its maximum computing performance was measured at 326 Gflops. The authors conclude that PakCarik is ready to be deployed in real-world applications and it can be made even more powerful by adding more GPU cards in it.