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Posts

Aug, 7

Real-Time High-Performance Computing for Embedded Control Systems

Critical real-time systems include a wide spectrum of computer systems whose correct behavior is dictated not only by correct functionality but also by their timely execution with respect to predefined deadlines. The increasing demand for higher performance in these systems has led the industry to recently include embedded Graphics Processing Units (GPUs), mainly for machine […]
Aug, 7

COX: Exposing CUDA Warp-Level Functions to CPUs

As CUDA becomes the de facto programming language among data parallel applications such as high-performance computing or machine learning applications, running CUDA on other platforms becomes a compelling option. Although several efforts have attempted to support CUDA on devices other than NVIDIA GPUs, due to extra steps in the translation, the support is always a […]
Jul, 24

Demystifying Dependency Bugs in Deep Learning Stack

Recent breakthroughs in deep learning (DL) techniques have stimulated significant growth in developing DL-enabled applications. These DL applications, built upon a heterogeneous and complex DL stack (e.g., Nvidia GPU, Linux, CUDA driver, Python runtime, and TensorFlow), are subject to software and hardware dependencies across the DL stack. A persistent challenge in dependency management across the […]
Jul, 24

CPU-GPU Layer-Switched Low Latency CNN Inference

Convolutional Neural Networks (CNNs) inference on Heterogeneous Multi-Processor System-on-Chips (HMPSoCs) in edge devices represent cutting-edge embedded machine learning. Embedded CPU and GPU within an HMPSoC can both perform inference using CNNs. However, common practice is to run a CNN on the HMPSoC component (CPU or GPU) provides the best performance (lowest latency) for that CNN. […]
Jul, 24

FPGA Accelerators on Heterogeneous Systems: An Approach Using High Level Synthesis

The emergence of FPGAs in the High-Performance Computing domain is arising thanks to their promise of better energy efficiency and low control latency, compared with other devices such as CPUs or GPUs. Albeit these benefits, their complete inclusion into HPC systems still faces several challenges. First, FPGA complexity means its programming more difficult compared to […]
Jul, 24

Theseus: A Library for Differentiable Nonlinear Optimization

We present Theseus, an efficient application-agnostic open source library for differentiable nonlinear least squares (DNLS) optimization built on PyTorch, providing a common framework for end-to-end structured learning in robotics and vision. Existing DNLS implementations are application specific and do not always incorporate many ingredients important for efficiency. Theseus is application-agnostic, as we illustrate with several […]
Jul, 24

On Scheduling Ring-All-Reduce Learning Jobs in Multi-Tenant GPU Clusters with Communication Contention

Powered by advances in deep learning (DL) techniques, machine learning and artificial intelligence have achieved astonishing successes. However, the rapidly growing needs for DL also led to communication- and resource-intensive distributed training jobs for large-scale DL training, which are typically deployed over GPU clusters. To sustain the ever-increasing demand for DL training, the so-called "ring-all-reduce" […]
Jul, 17

Reducing Synchronous GPU Memory Transfers: Design and implementation of a Futhark compiler optimisation

We present a series of dataflow dependent program transformations that reduce memory transfers between a GPU and its host, and show how the problem of minimising memory transfers to the host amounts to finding minimum vertex cuts in a series of data dependency graphs. We provide a specialised algorithm to solve these minimisation problems, based […]
Jul, 17

Heterogeneous Energy-aware Load Balancing for Industry 4.0 and IoT Environments

With the improvement of global infrastructure, Cyber-Physical Systems (CPS) have become an important component of Industry 4.0. Both the application as well as the machine work together to improve the task of interdependencies. Machine learning methods in CPS require the monitoring of computational algorithms, including adopting optimizations, fine-tuning cyber systems, improving resource utilization, as well […]
Jul, 17

Just-in-Time Compilation and Link-Time Optimization for OpenMP Target Offloading

Following the mass adoption of external accelerators for high performance computing, the overall performance of many applications has become increasingly dependent on relatively small accelerated kernels. As static analysis is fundamentally limited by dynamic values and external definitions, standard ahead-of-time compilation is not always sufficient to achieve the best performance. Furthermore, many users looking to […]
Jul, 17

The OpenMP Cluster Programming Model

Despite the various research initiatives and proposed programming models, efficient solutions for parallel programming in HPC clusters still rely on a complex combination of different programming models (e.g., OpenMP and MPI), languages (e.g., C++ and CUDA), and specialized runtimes (e.g., Charm++ and Legion). On the other hand, task parallelism has shown to be an efficient […]
Jul, 17

High Performance Simulation for Scalable Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning experiments and open-source training environments are typically limited in scale, supporting tens or sometimes up to hundreds of interacting agents. In this paper we demonstrate the use of Vogue, a high performance agent based model (ABM) framework. Vogue serves as a multi-agent training environment, supporting thousands to tens of thousands of interacting […]

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