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To deliver high performance in power limited systems, architects have turned to using heterogeneous systems, either CPU+GPU or mixed CPU-hardware systems. However, in systems with different processor types and task affinities, scheduling…
The growing capacity of integration allows to instantiate hundreds of soft-core processors in a single FPGA to create a reconfigurable multiprocessing system. Lately, FPGAs have been proven to give a higher energy efficiency than…
When designing modern embedded computing systems, most software programmers choose to use multicore processors, possibly in combination with general-purpose graphics processing units (GPGPUs) and/or hardware accelerators. They also often…
High-performance computing systems are more and more often based on accelerators. Computing applications targeting those systems often follow a host-driven approach in which hosts offload almost all compute-intensive sections of the code…
One of the most critical aspects of integrating loosely-coupled accelerators in heterogeneous SoC architectures is orchestrating their interactions with the memory hierarchy, especially in terms of navigating the various cache-coherence…
We propose a CPU-GPU heterogeneous computing method for solving time-evolution partial differential equation problems many times with guaranteed accuracy, in short time-to-solution and low energy-to-solution. On a single-GH200 node, the…
Edge machine learning involves the deployment of learning algorithms at the network edge to leverage massive distributed data and computation resources to train artificial intelligence (AI) models. Among others, the framework of federated…
While neural network hardware accelerators provide a substantial amount of raw compute throughput, the models deployed on them must be co-designed for the underlying hardware architecture to obtain the optimal system performance. We present…
Context. Many Internet content platforms, such as Spotify and YouTube, provide their services via both native and Web apps. Even though those apps provide similar features to the end user, using their native version or Web counterpart might…
In this dissertation, we propose a memory and computing coordinated methodology to thoroughly exploit the characteristics and capabilities of the GPU-based heterogeneous system to effectively optimize applications' performance and privacy.…
Future experiments in high-energy physics will pose stringent requirements to computing, in particular to real-time data processing. As an example, the CBM experiment at FAIR Germany intends to perform online data selection exclusively in…
Modern enterprises rely on data management systems to collect, store, and analyze vast amounts of data related with their operations. Nowadays, clusters and hardware accelerators (e.g., GPUs, TPUs) have become a necessity to scale with the…
As we reach exascale, production High Performance Computing (HPC) systems are increasing in complexity. These systems now comprise multiple heterogeneous computing components (CPUs and GPUs) utilized through diverse, often vendor-specific…
Today, using multiple heterogeneous accelerators efficiently from applications and high-level frameworks, such as TensorFlow and Caffe, poses significant challenges in three respects: (a) sharing accelerators, (b) allocating available…
In this proceedings we demonstrate some advantages of a top-bottom approach in the development of hardware-accelerated code. We start with an autogenerated hardware-agnostic Monte Carlo generator, which is parallelized in the event axis.…
Heterogeneous systems are present from powerful supercomputers, to mobile devices, including desktop computers, thanks to their excellent performance and energy consumption. The ubiquity of these architectures in both desktop systems and…
Energy efficiency and computing flexibility are some of the primary design constraints of heterogeneous computing. In this paper, we present FlashAbacus, a data-processing accelerator that self-governs heterogeneous kernel executions and…
Transformers have revolutionized deep learning and generative modeling to enable unprecedented advancements in natural language processing tasks and beyond. However, designing hardware accelerators for executing transformer models is…
In this paper, we present an OpenCL-based heterogeneous implementation of a computer vision algorithm -- image inpainting-based object removal algorithm -- on mobile devices. To take advantage of the computation power of the mobile…
Cutting edge classical computing today relies on a combination of CPU-based computing with a strong reliance on accelerators. In particular, high-performance computing (HPC) and machine learning (ML) rely heavily on acceleration via GPUs…