high performance computing on graphics processing units: hgpu.org

Rewriting code for cleanliness, API changes, and new programming models is a common, yet time-consuming task. This is important for HPC applications that desire performance portability in particular, since these applications are usually very long lived and wish to run on many architectures, so they need to be written such that they can make good […]

Several open-source systems, such as Flower and NVIDIA FLARE, have been developed in recent years while focusing on different aspects of federated learning (FL). Flower is dedicated to implementing a cohesive approach to FL, analytics, and evaluation. Over time, Flower has cultivated extensive strategies and algorithms tailored for FL application development, fostering a vibrant FL […]

The increasing adoption of large language models (LLMs) has created a pressing need for an efficient, secure and private serving infrastructure, which allows researchers to run open-source or custom fine-tuned LLMs and ensures users that their data remains private and is not stored without their consent. While high-performance computing (HPC) systems equipped with state-of-the-art GPUs […]

In this chapter, we describe the Parallel Sparse Computation Toolkit (PSCToolkit), a suite of libraries for solving large-scale linear algebra problems in an HPC environment. In particular, we focus on the tools provided for the solution of symmetric and positive-definite linear systems using up to 8192 GPUs on the EuroHPC-JU Leonardo supercomputer. PSCToolkit is an […]

We introduce Diffuse, a system that dynamically performs task and kernel fusion in distributed, task-based runtime systems. The key component of Diffuse is an intermediate representation of distributed computation that enables the necessary analyses for the fusion of distributed tasks to be performed in a scalable manner. We pair task fusion with a JIT compiler […]

Bayesian optimization is a powerful method for automating tuning of compilers. The complex landscape of autotuning provides a myriad of rarely considered structural challenges for black-box optimizers, and the lack of standardized benchmarks has limited the study of Bayesian optimization within the domain. To address this, we present CATBench, a comprehensive benchmarking suite that captures […]

Data management has seen rapid evolution during the last years, influenced by factors such as data explosion, the prevalence of machine and deep learning, the slowdown of Moore’s law and the popularity of hardware accelerators. Data processing systems are trying to adapt to all these trends by building monolithic and highly specialized systems, which are […]

Since the 1990’s many implementations of polyhedral compilers have been written and distributed, either as source-to-source translating compilers or integrated into wider purpose compilers. This paper provides a survey on those various available implementations as of today, 2024. We list and describe most commonly available polyhedral schedulers and compiler implementations. Then, we compare the general-purpose […]

The explosion in Machine Learning (ML) over the past ten years has led to a dramatic increase in demand for GPUs to train ML models. Because it is prohibitively expensive for most users to build and maintain a large GPU cluster, large cloud providers (Microsoft Azure, Amazon AWS, Google Cloud) have seen explosive growth in […]

Transformers and LLMs have seen rapid adoption in all domains. Their sizes have exploded to hundreds of billions of parameters and keep increasing. Under these circumstances, the training of transformers is slow and often takes in the order of weeks or months. Thanks to 3D model parallelism (data, pipeline, and tensor-level parallelism), the training can […]

Astronomical image subtraction is a method for generating a difference image from two images, which covers the same area but taken at different times, in order to see changes over time. Due to the images being taken at different times, one of the images has to be convolved, to match the atmospheric conditions of the […]

The proliferation of deep learning in various domains has led to remarkable advancements in artificial intelligence applications, such as large-language models for scientific use cases. However, the concomitant exponential growth in computational demands, driven by the development of ever-larger deep learning models, presents significant challenges in terms of resource consumption and sustainability. This dissertation addresses […]