Personne
Aurélien François Gilbert Bloch
Cette personne n’est plus à l’EPFL
Publications associées (9)
Compilation and Design Space Exploration of Dataflow Programs for Heterogeneous CPU-GPU Platforms
Aurélien François Gilbert Bloch
Today's continued increase in demand for processing power, despite the slowdown of Moore's law, has led to an increase in processor count, which has resulted in energy consumption and distribution problems. To address this, there is a growing trend toward ...
EPFL2023Design Space Exploration for Partitioning Dataflow Program on CPU-GPU Heterogeneous System
Marco Mattavelli, Simone Casale Brunet, Aurélien François Gilbert Bloch
Dataflow programming is a methodology that enables the development of high-level, parametric programs that are independent of the underlying platform. This approach is particularly useful for heterogeneous platforms, as it eliminates the need to rewrite ap ...
SPRINGER2023Dynamic SIMD Parallel Execution on GPU from High-Level Dataflow Synthesis
Marco Mattavelli, Simone Casale Brunet, Aurélien François Gilbert Bloch
Developing and fine-tuning software programs for heterogeneous hardware such as CPU/GPU processing platforms comprise a highly complex endeavor that demands considerable time and effort of software engineers and requires evaluating various fundamental comp ...
MDPI2022Performance Estimation of High-Level Dataflow Program on Heterogeneous Platforms by Dynamic Network Execution
Marco Mattavelli, Simone Casale Brunet, Aurélien François Gilbert Bloch
The performance of programs executed on heterogeneous parallel platforms largely depends on the design choices regarding how to partition the processing on the various different processing units. In other words, it depends on the assumptions and parameters ...
MDPI2022SIMD Parallel Execution on GPU from High-Level Dataflow Synthesis
Marco Mattavelli, Simone Casale Brunet, Aurélien François Gilbert Bloch
Writing and optimizing application software for heterogeneous platforms including GPU units is a very difficult task that requires designer efforts and resources to consider several key elements to obtain good performance. Dataflow programming has shown to ...
2022Inter-actions parallel execution on GPU from high-level dataflow synthesis
Marco Mattavelli, Simone Casale Brunet, Aurélien François Gilbert Bloch
Recent GPU architectures make available numbers of parallel processing units that exceed by orders of magnitude the ones offered by CPU architectures. Whereas programs written using dataflow programming languages are well suited for programming heterogeneo ...
IEEE2022Performance Estimation of High-Level Dataflow Program on Heterogeneous Platforms
Marco Mattavelli, Simone Casale Brunet, Aurélien François Gilbert Bloch
The performance of programs written in languages following the dataflow model of computation (MoC) largely depends on the configuration (partitioning, mapping, scheduling, buffer dimensioning) chosen during the synthesis stages. Furthermore, this programmi ...
2022Programming Heterogeneous CPU-GPU Systems by High-Level Dataflow Synthesis
Marco Mattavelli, Endri Bezati, Aurélien François Gilbert Bloch
Heterogeneous processing platforms combining in various architectures CPUs, GPUs, and programmable logic, are continuously evolving providing at each generation higher theoretical levels of computing performance. However, the challenge of how efficiently s ...
IEEE2020Composite Data Types in Dynamic Dataflow Languages as Copyless Memory Sharing Mechanism
Marco Mattavelli, Endri Bezati, Aurélien François Gilbert Bloch
This paper presents new optimization approaches aiming at reducing the impact of memory accesses on the performance of dataflow programs. The approach is based on introducing a high level management of composite data types in dynamic dataflow programming l ...
2019