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Posts

Dec, 8

PyTorch: An Imperative Style, High-Performance Deep Learning Library

Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it provides an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing […]
Dec, 8

The BondMachine toolkit: Enabling Machine Learning on FPGA

The BondMachine (BM) is an innovative prototype software ecosystem aimed at creating facilities where both hardware and software are co-designed, guaranteeing a full exploitation of fabric capabilities (both in terms of concurrency and heterogeneity) with the smallest possible power dissipation. In the present paper we will provide a technical overview of the key aspects of […]
Dec, 8

Masivo: Parallel Simulation Model Based on OpenCL for Massive Public Transportation Systems’ Routes

There is a large number of tools for the simulation of traffic and routes in public transport systems. These use different simulation models (macroscopic, microscopic, and mesoscopic). Unfortunately, these simulation tools are limited when simulating a complete public transport system, which includes all its buses and routes (up to 270 for the London Underground). The […]
Dec, 8

GGNN: Graph-based GPU Nearest Neighbor Search

Approximate nearest neighbor (ANN) search in high dimensions is an integral part of several computer vision systems and gains importance in deep learning with explicit memory representations. Since PQT and FAISS started to leverage the massive parallelism offered by GPUs, GPU-based implementations are a crucial resource for today’s state-of-the-art ANN methods. While most of these […]
Dec, 8

GPU Computing with Python: Performance, Energy Efficiency and Usability

In this work, we examine the performance, energy efficiency and usability when using Python for developing HPC codes running on the GPU. We investigate the portability of performance and energy efficiency between CUDA and OpenCL; between GPU generations; and between low-end, mid-range and high-end GPUs. Our findings show that the impact of using Python is […]
Dec, 1

FBLAS: Streaming Linear Algebra Kernels on FPGA

Reconfigurable hardware represents an attractive alternative to load-store architectures, as it allows eliminating expensive control and data movement overheads in computations. In practice, these devices are often not considered in the highperformance computing community, due to the steep learning curve and low productivity of hardware design, and the lack of available library support for fundamental […]
Dec, 1

Scalable Applications on Heterogeneous System Architectures: A Systematic Performance Analysis Framework

The efficient parallel execution of scientific applications is a key challenge in high-performance computing (HPC). With growing parallelism and heterogeneity of compute resources as well as increasingly complex software, performance analysis has become an indispensable tool in the development and optimization of parallel programs. It is a recurring task as HPC systems and their software […]
Dec, 1

FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things

The growing number of low-power smart devices in the Internet of Things is coupled with the concept of "Edge Computing", that is moving some of the intelligence, especially machine learning, towards the edge of the network. Enabling machine learning algorithms to run on resource-constrained hardware, typically on low-power smart devices, is challenging in terms of […]
Dec, 1

Titan: A Parallel Asynchronous Library for Multi-Agent and Soft-Body Robotics using NVIDIA CUDA

While most robotics simulation libraries are built for low-dimensional and intrinsically serial tasks, soft-body and multi-agent robotics have created a demand for simulation environments that can model many interacting bodies in parallel. Despite the increasing interest in these fields, no existing simulation library addresses the challenge of providing a unified, highly-parallelized, GPU-accelerated interface for simulating […]
Dec, 1

FusionStitching: Boosting Execution Efficiency of Memory Intensive Computations for DL Workloads

Performance optimization is the art of continuous seeking a harmonious mapping between the application domain and hardware. Recent years have witnessed a surge of deep learning (DL) applications in industry. Conventional wisdom for optimizing such workloads mainly focus on compute intensive ops (GEMM, Convolution, etc). Yet we show in this work, that the performance of […]
Nov, 24

Understanding the Performance of HPC Applications

High performance computing is an important asset to scientific research, enabling the study of phenomena such as nuclear physics or climate change, that are difficult or impossible to be studied in traditional experiments or allowing researchers to utilize large amounts of data from experiments such as the Large Hadron Collider. No matter the use of […]
Nov, 24

Benchmarking Deep Learning Models on Jetson TX2

In conclusion, the present work brings an overview of artificial intelligence and, mainly, deep learning fields with a focus on image recognition and the history behind the models and techniques present nowadays. Beyond that, we explored how embedded hardware work with the new scenarios that AI brings to the table and how companies are developing […]

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