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Posts

Jun, 2

An implementation of tensor product patch smoothers on GPU

We present a GPU implementation of vertex-patch smoothers for higher order finite element methods in two and three dimensions. Analysis shows that they are not memory bound with respect to GPU DRAM, but with respect to on-chip scratchpad memory. Multigrid operations are optimized through localization and reorganized local operations in on-chip memory, achieving minimal global […]
Jun, 2

A Survey of Cloud-Based GPU Threats and Their Impact on AI, HPC, and Cloud Computing

Graphics processing units (GPUs) are the hardware engines driving the AI revolution. Large language model (LLM)-powered generative AI (GenAI) became mainstream with the public release of OpenAI’s ChatGPT. AI usage has given rise to innovative AI-powered applications for businesses, productivity, image generation, video generation, data analysis, and social media, among others. Powering AI applications are […]
May, 26

Enabling full-speed random access to the entire memory on the A100 GPU

We describe some features of the A100 memory architecture. In particular, we give a technique to reverse-engineer some hardware layout information. Using this information, we show how to avoid TLB issues to obtain full-speed random HBM access to the entire memory, as long as we constrain any particular thread to a reduced access window of […]
May, 26

ArchesWeather: An efficient AI weather forecasting model at 1.5° resolution

One of the guiding principles for designing AI-based weather forecasting systems is to embed physical constraints as inductive priors in the neural network architecture. A popular prior is locality, where the atmospheric data is processed with local neural interactions, like 3D convolutions or 3D local attention windows as in Pangu-Weather. On the other hand, some […]
May, 26

GPU Implementations for Midsize Integer Addition and Multiplication

This paper explores practical aspects of using a high-level functional language for GPU-based arithmetic on “midsize” integers. By this we mean integers of up to about a quarter million bits, which is sufficient for most practical purposes. The goal is to understand whether it is possible to support efficient nested-parallel programs with a small, flexible […]
May, 26

STuning-DL: Model-Driven Autotuning of Sparse GPU Kernels for Deep Learning

The relentless growth of modern Machine Learning models has spurred the adoption of sparsification techniques to simplify their architectures and reduce the computational demands. Network pruning has demonstrated success in maintaining original network accuracy while shedding significant portions of the original weights. However, leveraging this sparsity efficiently remains challenging due to computational irregularities, particularly in […]
May, 26

Kernel-Centric Optimizations for Deep Neural Networks on GPGPU

Deep learning has achieved remarkable success across various domains, ranging from computer vision to healthcare. General-Purpose Graphics Processing Unit (GPGPU) is one of the major driving forces behind this revolution. GPGPUs offer massive parallel computational power, enabling the training and deployment of large-scale neural networks within practical time and resource constraints. Their programmability also enables […]
May, 20

Assessing Intel OneAPI capabilities and cloud-performance for heterogeneous computing

This work presents a performance-oriented study of a heterogeneous application developed with Intel OneAPI to solve two well-known diffusion problems: heat diffusion and image denoising. We have explored CPU+iGPU and CPU+FPGA schemes, applying dynamic load balancing and conducting experiments on Intel DevCloud. The results demonstrate that the CPU+iGPU scheme outperforms the execution times achieved by […]
May, 20

From GPUs to AI and quantum: three waves of acceleration in bioinformatics

The enormous growth in the amount of data generated by the life sciences is continuously shifting the field from model-driven science towards data-driven science. The need for efficient processing has led to the adoption of massively parallel accelerators such as graphics processing units (GPUs). Consequently, the development of bioinformatics methods nowadays often heavily depends on […]
May, 20

Hierarchical Resource Partitioning on Modern GPUs: A Reinforcement Learning Approach

GPU-based heterogeneous architectures are now commonly used in HPC clusters. Due to their architectural simplicity specialized for data-level parallelism, GPUs can offer much higher computational throughput and memory bandwidth than CPUs in the same generation do. However, as the available resources in GPUs have increased exponentially over the past decades, it has become increasingly difficult […]
May, 20

Predicting NVIDIA’s Next-Day Stock Price: A Comparative Analysis of LSTM, MLP, ARIMA, and ARIMA-GARCH Models

Forecasting stock prices remains a considerable challenge in financial markets, bearing significant implications for investors, traders, and financial institutions. Amid the ongoing AI revolution, NVIDIA has emerged as a key player driving innovation across various sectors. Given its prominence, we chose NVIDIA as the subject of our study.
May, 20

Workload Scheduling on Heterogeneous Devices

Hardware accelerators have become the backbone of many cloud and HPC workloads, but workloads tend to statically choose accelerators leaving devices unused while others are oversubscribed. We propose a holistic framework that allows a computational kernel to span across multiple devices on a node, as well as multiple applications being scheduled on the same node. […]

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