Can TornadoVM run Matrix Multiply faster than OpenCL Native?
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This article explores how TornadoVM, a Java parallel programming framework, can outperform OpenCL code on GPUs using the Matrix Multiplication application as an example.
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2024
Using oneAPI Construction Kit and TornadoVM to accelerate Java Programs on x86, ARM and RISC-V CPUs
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Running TornadoVM via the oneAPI Construction Kit for Intel, ARM and RISC-V CPUs.
Book Review: JVM Performance Engineering
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Running TornadoVM on CPUs and FPGAs via oneAPI
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This post shows the main steps to install and run TornadoVM on CPUs and FPGAs using the Intel oneAPI runtime for OpenCL.
Multi-device & Multi-backend TornadoVM
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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.
The TornadoVM Programming Model Explained
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You are a Java developer and you want to access GPUs? In this post I explain how by using TornadoVM.
Running TornadoVM within IntelliJ
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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.
2023
Installing the NVIDIA Drivers and CUDA 12.3 on Fedora 39 with Secure Boot Enabled
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Installing the NVIDIA Drivers and CUDA 12.3 on Fedora 39 with Secure Boot Enabled.
Accessible Dynamic SPIR-V Code Generation from Java
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Dynamic SPIR-V Code Generation from Java. Why do we need this and how can be used?
Unified Shared Memory: Friend or Fue?
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Unified Shared Memory: Friend or Fue? Understanding the Implications of Unified Memory on Managed
Configuration of the NVIDIA and Intel GPU drivers for RHEL9
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This post shows the installation steps to obtain NVIDIA CUDA and Intel OpenCL and Level Zero runtimes to run applications on GPUs with RHEL.
Running TornadoVM on NVIDIA Jetson Nano
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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.
2022
Enabling Transparent Acceleration of Big Data Frameworks Using Heterogeneous Hardware
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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.
Exploring Level Zero resources: repositories and purpose
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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
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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.
TornadoVM Internals: Java APIs for Compiling Java methods to SPIR-V and running on GPUs via Level Zero
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This post shows how to use the internal APIs to interact directly with the TornadoVM JIT compiler interface and runtime system.
Running Java Programs on XPUs with TornadoVM via Docker
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In this post, we will show how to launch and accelerate Java programs on heterogeneous hardware via TornadoVM with minimal configuration using pre-built Docker images
Running TornadoVM on Intel GPUs using Windows Subsystem for Linux (WSL) for Windows 11
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In this post, I will show you how we can enable TornadoVM to run on Intel HD Graphics via the OpenCL and SPIR-V Backends within WSL using Windows 11
Overall Performance of Unified Shared Memory Types with Level Zero on Intel Integrated GPUs
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Does share memory really impact performance if we measure end-to-end applications on GPUs? In this post, we try to answer this question.
Understanding Memory Allocation Size Limitations with Level Zero
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In this post we want to explore the memory capabilities of the Level Zero API, and, examine its constraints with respect to memory allocation.
Profiling OpenCL and SPIRV code from TornadoVM using VTune
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Profiling OpenCL and SPIRV code from TornadoVM using VTune : https://jjfumero.github.io/posts/2022/02/profiling-tornadovm-with-intel-vtune/
2021
Measuring Kernel Time and Data Transfers with Level Zero
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Measuring Kernel Time and Data Transfers with Level Zero : https://jjfumero.github.io/posts/2021/09/timers-with-level-zero/
Introduction to Level Zero API for Heterogeneous Programming
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Overview of the Intel Level-Zero API and a practical example to dispatch a SPIR-V kernel on the Intel HD Graphics: https://jjfumero.github.io/posts/2021/09/introduction-to-level-zero/