This work explores collecting performance metrics and leveraging the output for prediction on a memory-intensive parallel image classification algorithm - Inception v3 (or Inception3 ). Experimental results were collected by nvidia-smi on a computational node DGX-1, equipped with eight Tesla V100 Graphic Processing Units (GPUs). Time series analysis was performed on the GPU utilization data taken, for multiple runs, of Inception3’s image classification algorithm (see Figure 1). The time series model applied was Seasonal Autoregressive Integrated Moving Average Exogenous (SARIMAX)
Thesis is aimed at the possibility of utilization of extreme learning machines and echo state networ...
Uncovering repeated behavior in time series is an important problem in many domains such as medicine...
Data Mining allows one to analyze large amounts of data. With increasing amounts of data being colle...
This paper introduces a predictive modeling framework to estimate the performance of GPUs during pre...
High Performance Computing via General Purpose Graphical Processing Unit (GPU) is a potential instru...
The significant growth in computational power of mod-ern Graphics Processing Units(GPUs) coupled wit...
As the new generation of smart sensors is evolving towards high sampling acquisitions systems, the ...
Through the algorthmic design patterns of data parallelism and task parallelism, the graphics proces...
Heterogeneous processing using GPUs is here to stay and today spans mobile devices, laptops, and ...
To improve the power consumption of parallel applications at the runtime, modern processors provide ...
The parallel and distributed platforms of High Performance Computing available today have became mor...
CPUs and dedicated accelerators (namely GPUs and FPGAs) continue to grow increasingly large and comp...
This paper introduces a predictive modeling framework for GPU performance. The key innovation underl...
High-level tools for analyzing and predicting the performance GPU-accelerated applications are scarc...
Over the past several years, graphics processing units (GPU) have increasingly been viewed as the fu...
Thesis is aimed at the possibility of utilization of extreme learning machines and echo state networ...
Uncovering repeated behavior in time series is an important problem in many domains such as medicine...
Data Mining allows one to analyze large amounts of data. With increasing amounts of data being colle...
This paper introduces a predictive modeling framework to estimate the performance of GPUs during pre...
High Performance Computing via General Purpose Graphical Processing Unit (GPU) is a potential instru...
The significant growth in computational power of mod-ern Graphics Processing Units(GPUs) coupled wit...
As the new generation of smart sensors is evolving towards high sampling acquisitions systems, the ...
Through the algorthmic design patterns of data parallelism and task parallelism, the graphics proces...
Heterogeneous processing using GPUs is here to stay and today spans mobile devices, laptops, and ...
To improve the power consumption of parallel applications at the runtime, modern processors provide ...
The parallel and distributed platforms of High Performance Computing available today have became mor...
CPUs and dedicated accelerators (namely GPUs and FPGAs) continue to grow increasingly large and comp...
This paper introduces a predictive modeling framework for GPU performance. The key innovation underl...
High-level tools for analyzing and predicting the performance GPU-accelerated applications are scarc...
Over the past several years, graphics processing units (GPU) have increasingly been viewed as the fu...
Thesis is aimed at the possibility of utilization of extreme learning machines and echo state networ...
Uncovering repeated behavior in time series is an important problem in many domains such as medicine...
Data Mining allows one to analyze large amounts of data. With increasing amounts of data being colle...