Talks and presentations
Past talks - abstracts and links
Designing Parallel Programming APIs for Heterogeneous Hardware on top of Managed Runtime Systems
Talk, University of La Laguna, Tenerife, Spain, ULL, Tenerife, Spain
From CPU to GPU and FPGAs: Supercharging Java Applications with TornadoVM
Talk, JVMLS 2023 - Oracle Santa Clara, CA, Santa Clara, CA, US
TornadoVM: Multi-Backend Hardware Acceleration Framework for Java
Talk, oneAPI Language SIG - March 2023, Online
Boosting Performance of Java programs by Running on GPUs and FPGA via TornadoVM
Talk, Jax Mainz 2022, Mainz, Germany
TornadoVM: Transparent Hardware Acceleration for Java…and Beyond!
Talk, Devoxx Ukraine 2021, Virtual
Level Up Your Java Performance with TornadoVM
Talk, QCon Plus 2021, Virtual
Transparent Heterogeneous Computing for Java via TornadoVM @ NYJavaSIG
Talk, NYJavaSig, Virtual, Virtual
Slides available here
Rethinking Parallel Programming APIs: Towards Searching for the Gold API
Invited Talk, University of Kent - Seminars , Virtual
Running Parallel Bytecode Interpreters on Heterogeneous Hardware
Talk, MoreVMs 2020. Workshop collocated with Programming 2020. Porto, Portugal, Virtual
TornadoVM: Java for GPUs and FPGAs @QCon-London
Talk, QCon-London 2020, London, UK
Slides available here
TornadoVM: A virtual machine for exploiting high performance heterogeneous hardware
Talk, Joker Conference 2019, Saint Petersburg, Russia
Talk about TornadoVM given at Joker<?> 2019 Conference.
TornadoVM: A Virtual Machine for Exploiting High-Performance Heterogeneous Hardware of Java Programs
Talk, JVMLS 2019, Oracle, Santa Clara, CA, US
Talk about TornadoVM given at the JVMLS 2019 workshop.
Invited talk at ARM - Exploiting Heterogeneous Hardware from Managed Runtime Languages
Talk, Invited talk at ARM, Cambridge, UK, Cambridge, UK
The proliferation of heterogeneous hardware in recent years means that every system we program is likely to include a mix of computing elements; each of these with different characteristics. This trend has been accompanied by changes in software development norms that do not necessarily favor programmers. A prime example is the two most popular heterogeneous programming languages, CUDA and OpenCL, which expose several low-level features to the API making them difficult to use by non-expert users.
Invited talk at MSR - Tornado VM: A Virtual Machine for Exploiting High-Performance Heterogeneous Hardware of Java Programs
Talk, Invited talk at Microsoft Research, Cambridge, UK, Cambridge, UK
The proliferation of heterogeneous hardware in recent years means that every system we program is likely to include a mix of computing elements; each of these with different characteristics. This trend has been accompanied by changes in software development norms that do not necessarily favor programmers. A prime example is the two most popular heterogeneous programming languages, CUDA and OpenCL, which expose several low-level features to the API making them difficult to use by non-expert users.
Towards Practical Heterogeneous Virtual Machines
Talk, MoreVMs 2018, Nice France, Nice, France
Heterogeneous computing emerged as a means to achieve higher performance and energy efficiency. However, this trend has been accompanied by changes in software development norms that do not necessarily favour programmers. A prime example is the two most popular heterogeneous programming languages, CUDA and OpenCL, which expose several low-level features to the API making them difficult to use by non-expert users.
Invited Talk - OpenCL Just-In-Time Compilation for Dynamic Programming Languages
Talk, University of Glasgow (Glasgow Parallelism Group), Glasgow, UK
In this talk we present a technique to automatically offload parts of the input program written in a dynamic language into OpenCL without any changes in the original source code. Our preliminary results show we achieve speedups of up to 150x when using the GPU (3) OpenCL JIT Compilation for Dynamic Programming Languages.
Invited Talk at Edinburgh University - FastR-Flink
Talk, Invited talk Edinburgh R Users Group, The Unversity of Edinburgh, Edinburgh, UK
During the past few years R has become an important language for data analysis, data representation and visualization. R is a very expressive language which combines functional and dynamic aspects, with laziness and object oriented programming. However, the default Rimplementation is neither fast nor distributed, both features crucial for “big data” processing.
Invited Talk - FastR-Flink: A compiler based approach for distributed computing in R
Talk, Invited at at TU Berlin, TU Berlin, Germany
During the past few years R has become an important language for data analysis, data representation and visualization. R is a very expressive language which combines functional and dynamic aspects, with laziness and object oriented programming. However, the default Rimplementation is neither fast nor distributed, both features crucial for “big data” processing.