Aurélien François Gilbert Bloch

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The possibility of using the increasing computing power available in cloud infrastructures requires the development of new approaches for application software development and optimization. Emerging edge computing paradigms offer the possibility of reducing ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Aurélien François Gilbert Bloch

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Methodologies for Synthesizing and Analyzing Dynamic Dataflow Programs in Heterogeneous Systems for Edge Computing

Compilation and Design Space Exploration of Dataflow Programs for Heterogeneous CPU-GPU Platforms

Performance Estimation of High-Level Dataflow Program on Heterogeneous Platforms by Dynamic Network Execution

Design Space Exploration for Partitioning Dataflow Program on CPU-GPU Heterogeneous System

Inter-actions parallel execution on GPU from high-level dataflow synthesis

Performance Estimation of High-Level Dataflow Program on Heterogeneous Platforms

Dynamic SIMD Parallel Execution on GPU from High-Level Dataflow Synthesis

SIMD Parallel Execution on GPU from High-Level Dataflow Synthesis

Programming Heterogeneous CPU-GPU Systems by High-Level Dataflow Synthesis

Composite Data Types in Dynamic Dataflow Languages as Copyless Memory Sharing Mechanism