Juan Fumero, PhD

Juan Fumero, PhD

Passionate about compilers and parallelism. Research Fellow at The University of Manchester. I like running & playing synths & photography.

Posts by Tags

Academic Paper

Apache Flink

Enabling Transparent Acceleration of Big Data Frameworks Using Heterogeneous Hardware

21 minute read

Published:

Exploiting heterogeneous hardware for Big Data workloads is usually done by introducing new APIs, resulting in more complex programs to develop, understand, and maintain. But, what if we do not change/extend the original programming model? Is it possible? This post discusses a new approach to do so.

CPUs

CUDA

Running TornadoVM within IntelliJ

4 minute read

Published:

Running Java applications from existing IDEs can be a cumbersome process, especially if we need to specify shared libraries. In this post, I will explain how to get access to NVIDIA and Intel-integrated GPUs from IntelliJ using TornadoVM.

Installing CUDA, OpenCL and Level Zero in OpenSUSE Leap 15

6 minute read

Published:

In this post, we show how to install the NVIDIA drivers to get access to CUDA and OpenCL parallel programming frameworks and utilities for NVIDIA GPUs. We also show how to install the Intel compute-runtime drivers for accessing, via OpenCL and Level Zero, Intel Integrated Graphics.

Co-design Approach

Enabling Transparent Acceleration of Big Data Frameworks Using Heterogeneous Hardware

21 minute read

Published:

Exploiting heterogeneous hardware for Big Data workloads is usually done by introducing new APIs, resulting in more complex programs to develop, understand, and maintain. But, what if we do not change/extend the original programming model? Is it possible? This post discusses a new approach to do so.

Code Generation

Concurrency

Multi-device & Multi-backend TornadoVM

20 minute read

Published:

This post shows, via examples, how developers can benefit from these features, and reason about performance using the TornadoVM profiler to help us tune our applications.

Configuration

Running TornadoVM within IntelliJ

4 minute read

Published:

Running Java applications from existing IDEs can be a cumbersome process, especially if we need to specify shared libraries. In this post, I will explain how to get access to NVIDIA and Intel-integrated GPUs from IntelliJ using TornadoVM.

Deep Learning

Running TornadoVM on NVIDIA Jetson Nano

7 minute read

Published:

Did you know that TornadoVM can also run on ARM-based systems with NVIDIA GPUs? In this post, we will show how TornadoVM can be used on an NVIDIA Jetson Nano, a small, powerful computer designed for embedded artificial intelligence (AI) and machine learning (ML) applications.

Docker

Drivers

Installing CUDA, OpenCL and Level Zero in OpenSUSE Leap 15

6 minute read

Published:

In this post, we show how to install the NVIDIA drivers to get access to CUDA and OpenCL parallel programming frameworks and utilities for NVIDIA GPUs. We also show how to install the Intel compute-runtime drivers for accessing, via OpenCL and Level Zero, Intel Integrated Graphics.

FPGAs

Fedora39

GPGPU

GPU Drivers

GPU Profiling

GPUs

Multi-device & Multi-backend TornadoVM

20 minute read

Published:

This post shows, via examples, how developers can benefit from these features, and reason about performance using the TornadoVM profiler to help us tune our applications.

Hardware Acceleration

Enabling Transparent Acceleration of Big Data Frameworks Using Heterogeneous Hardware

21 minute read

Published:

Exploiting heterogeneous hardware for Big Data workloads is usually done by introducing new APIs, resulting in more complex programs to develop, understand, and maintain. But, what if we do not change/extend the original programming model? Is it possible? This post discusses a new approach to do so.

Hardware Accelerators

Multi-device & Multi-backend TornadoVM

20 minute read

Published:

This post shows, via examples, how developers can benefit from these features, and reason about performance using the TornadoVM profiler to help us tune our applications.

Heterogeneous Programming

Installation

Installing CUDA, OpenCL and Level Zero in OpenSUSE Leap 15

6 minute read

Published:

In this post, we show how to install the NVIDIA drivers to get access to CUDA and OpenCL parallel programming frameworks and utilities for NVIDIA GPUs. We also show how to install the Intel compute-runtime drivers for accessing, via OpenCL and Level Zero, Intel Integrated Graphics.

Intel GPUs

Running TornadoVM within IntelliJ

4 minute read

Published:

Running Java applications from existing IDEs can be a cumbersome process, especially if we need to specify shared libraries. In this post, I will explain how to get access to NVIDIA and Intel-integrated GPUs from IntelliJ using TornadoVM.

Intel HD Graphics

Intel Integrated GPUs

Intel Level Zero

Intel OpenCL

Intel VTune

IntelliJ

Running TornadoVM within IntelliJ

4 minute read

Published:

Running Java applications from existing IDEs can be a cumbersome process, especially if we need to specify shared libraries. In this post, I will explain how to get access to NVIDIA and Intel-integrated GPUs from IntelliJ using TornadoVM.

JIT Compilation

Java

Multi-device & Multi-backend TornadoVM

20 minute read

Published:

This post shows, via examples, how developers can benefit from these features, and reason about performance using the TornadoVM profiler to help us tune our applications.

Level Zero

Exploring Level Zero resources: repositories and purpose

2 minute read

Published:

Sometimes, it is not clear which Level Zero repository is the right one for our needs. In this post, we will explain each of the Level Zero public resources and what they are intended to be.

Installing CUDA, OpenCL and Level Zero in OpenSUSE Leap 15

6 minute read

Published:

In this post, we show how to install the NVIDIA drivers to get access to CUDA and OpenCL parallel programming frameworks and utilities for NVIDIA GPUs. We also show how to install the Intel compute-runtime drivers for accessing, via OpenCL and Level Zero, Intel Integrated Graphics.

Machine Learning

Running TornadoVM on NVIDIA Jetson Nano

7 minute read

Published:

Did you know that TornadoVM can also run on ARM-based systems with NVIDIA GPUs? In this post, we will show how TornadoVM can be used on an NVIDIA Jetson Nano, a small, powerful computer designed for embedded artificial intelligence (AI) and machine learning (ML) applications.

Managed Runtime Systems

Enabling Transparent Acceleration of Big Data Frameworks Using Heterogeneous Hardware

21 minute read

Published:

Exploiting heterogeneous hardware for Big Data workloads is usually done by introducing new APIs, resulting in more complex programs to develop, understand, and maintain. But, what if we do not change/extend the original programming model? Is it possible? This post discusses a new approach to do so.

MaxineVM

Memory Allocation

Memory Types

Multi-Backend

Multi-device & Multi-backend TornadoVM

20 minute read

Published:

This post shows, via examples, how developers can benefit from these features, and reason about performance using the TornadoVM profiler to help us tune our applications.

NVIDIA

Running TornadoVM within IntelliJ

4 minute read

Published:

Running Java applications from existing IDEs can be a cumbersome process, especially if we need to specify shared libraries. In this post, I will explain how to get access to NVIDIA and Intel-integrated GPUs from IntelliJ using TornadoVM.

NVIDIA CUDA

NVIDIA Jetson Nano

Running TornadoVM on NVIDIA Jetson Nano

7 minute read

Published:

Did you know that TornadoVM can also run on ARM-based systems with NVIDIA GPUs? In this post, we will show how TornadoVM can be used on an NVIDIA Jetson Nano, a small, powerful computer designed for embedded artificial intelligence (AI) and machine learning (ML) applications.

OpenCL

Installing CUDA, OpenCL and Level Zero in OpenSUSE Leap 15

6 minute read

Published:

In this post, we show how to install the NVIDIA drivers to get access to CUDA and OpenCL parallel programming frameworks and utilities for NVIDIA GPUs. We also show how to install the Intel compute-runtime drivers for accessing, via OpenCL and Level Zero, Intel Integrated Graphics.

OpenSUSE Leap 15

Installing CUDA, OpenCL and Level Zero in OpenSUSE Leap 15

6 minute read

Published:

In this post, we show how to install the NVIDIA drivers to get access to CUDA and OpenCL parallel programming frameworks and utilities for NVIDIA GPUs. We also show how to install the Intel compute-runtime drivers for accessing, via OpenCL and Level Zero, Intel Integrated Graphics.

Profiling

Programming Model

RHEL

Research

Resources

Exploring Level Zero resources: repositories and purpose

2 minute read

Published:

Sometimes, it is not clear which Level Zero repository is the right one for our needs. In this post, we will explain each of the Level Zero public resources and what they are intended to be.

Runtime System

SPEC

Exploring Level Zero resources: repositories and purpose

2 minute read

Published:

Sometimes, it is not clear which Level Zero repository is the right one for our needs. In this post, we will explain each of the Level Zero public resources and what they are intended to be.

SPIR-V

Timers

Toolkit

TornadoVM

Multi-device & Multi-backend TornadoVM

20 minute read

Published:

This post shows, via examples, how developers can benefit from these features, and reason about performance using the TornadoVM profiler to help us tune our applications.

Running TornadoVM within IntelliJ

4 minute read

Published:

Running Java applications from existing IDEs can be a cumbersome process, especially if we need to specify shared libraries. In this post, I will explain how to get access to NVIDIA and Intel-integrated GPUs from IntelliJ using TornadoVM.

Running TornadoVM on NVIDIA Jetson Nano

7 minute read

Published:

Did you know that TornadoVM can also run on ARM-based systems with NVIDIA GPUs? In this post, we will show how TornadoVM can be used on an NVIDIA Jetson Nano, a small, powerful computer designed for embedded artificial intelligence (AI) and machine learning (ML) applications.

Enabling Transparent Acceleration of Big Data Frameworks Using Heterogeneous Hardware

21 minute read

Published:

Exploiting heterogeneous hardware for Big Data workloads is usually done by introducing new APIs, resulting in more complex programs to develop, understand, and maintain. But, what if we do not change/extend the original programming model? Is it possible? This post discusses a new approach to do so.

Unified Memory

Unified Shared Memory

WSL

Windows 11

Windows WSL

level-zero

oneAPI

Exploring Level Zero resources: repositories and purpose

2 minute read

Published:

Sometimes, it is not clear which Level Zero repository is the right one for our needs. In this post, we will explain each of the Level Zero public resources and what they are intended to be.