GPU has emerged as the go-to accelerator for HPC workloads, however its power consumption has become a major limiting factor for further scaling HPC systems. An accurate understanding of GPU power consumption is essential for further improving its energy efficiency, and consequently reducing the associated carbon footprint. Despite the limited documentation and lack of understanding, NVIDIA GPUs’ built-in power sensor is widely used in energy-efficient computing research. Our study seeks to elucidate the internal mechanisms of the power readings provided by nvidia-smi and assess the accuracy of the measurements. We evaluated over 70 different GPUs across 12 architectural generations, and identified several unforeseen problems that can lead to drastic under/overestimation of energy consumed, for example on the A100 and H100 GPUs only 25% of the runtime is sampled. We proposed several mitigations that could reduce the energy measurement error by an average of 35% in the test cases we present.

We present an implementation of the overlap-and-save method, a method for the convolution of very long signals with short response functions, which is tailored to GPUs. We have implemented several FFT algorithms (using the CUDA programming language) which exploit GPU shared memory, allowing for GPU accelerated convolution. We compare our implementation with an implementation of the overlap-and-save algorithm utilizing the NVIDIA FFT library (cuFFT). We demonstrate that by using a shared memory based FFT we can achieved significant speed-ups for certain problem sizes and lower the memory requirements of the overlap-and-save method on GPUs.

The Square Kilometre Array (SKA) will be the largest radio telescope constructed to date and the largest Big Data project in the known Universe. The first phase of the project will generate 160 terabytes every second. This amounts to 5 zettabytes (5 million petabytes) of data that will be generated by the facility each year - a data rate equivalent to 5 times the estimated global internet traffic in 2015. These data need to be reduced and then continuously ingested by the SKA Science Data Processor (SDP). Within the SDP Consortium, we are contributing to various roles in the development of the telescope including building a lightweight end-to-end prototype of the major components of the SDP system - a project we call the SDP Integration Prototype (SIP). The aim is to build a mini, fully-operational SDP, for which we have been developing realistic SKA-like science pipelines that can handle these unprecedented data volumes.