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Related publications (30)

MOD2IR: High-Performance Code Generation for a Biophysically Detailed Neuronal Simulation DSL

Felix Schürmann, Pramod Shivaji Kumbhar, Omar Awile, Snehal Pramod Kumbhar, Ioannis Magkanaris

Advances in computational capabilities and large volumes of experimental data have established computer simulations of brain tissue models as an important pillar in modern neuroscience. Alongside, a variety of domain specific languages (DSLs) have been dev ...

ACM2023

E-Scan: Consuming Contextual Data with Model Plugins

Anastasia Ailamaki, Viktor Sanca

Extracting value and insights from increasingly heterogeneous data sources involves multiple systems combining and consuming the data. With multi-modal and context-rich data such as strings, text, videos, or images, the problem of standardizing the data mo ...

2023

SIMD 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 ...

2022

Dalton: Learned Partitioning for Distributed Data Streams

Anastasia Ailamaki, Eleni Zapridou, Ioannis Mytilinis

To sustain the input rate of high-throughput streams, modern stream processing systems rely on parallel execution. However, skewed data yield imbalanced load assignments and create stragglers that hinder scalability. Deciding on a static partitioning for a ...

2022

Parallel temporal signal processing enabled by polarization-multiplexed programmable THz metasurfaces

Romain Christophe Rémy Fleury, Ali Momeni

Under the trends of multifunctionality, tunability, and compactness in modern wave -based signal processors, in this paper, we propose a polarization-multiplexed graphene-based metasurface to realize distinct mathematical operators on the parallel time-dom ...

Optica Publishing Group2022

System Support for Robust Distributed Learning

Arsany Hany Abdelmessih Guirguis

Machine learning (ML) applications are ubiquitous. They run in different environments such as datacenters, the cloud, and even on edge devices. Despite where they run, distributing ML training seems the only way to attain scalable, high-quality learning. B ...

EPFL2022

From C/C plus plus Code to High-Performance Dataflow Circuits

Paolo Ienne, Andrea Guerrieri, Lana Josipovic

High-level synthesis (HLS) tools typically generate statically scheduled datapaths. Static scheduling implies that the resulting circuits have a hard time exploiting parallelism in code with potential memory dependences, with control dependences, or where ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Cockroach: an open-source tool for point cloud processing in cad

Yves Weinand, Julien Gamerro, Andrea Settimi, Petras Vestartas

In the architecture, engineering and construction (AEC) sector, the use of point cloud data is not a novelty. Usually employed to retrieve data for inspecting construction sites or retrofitting pre-existing buildings, sensors like LiDAR cameras have been k ...

Association for Computer-Aided Architectural Design Research in Asia (CAADRIA)2022

Efficient Analytical Query Processing on CPU-GPU Hardware Platforms

Periklis Chrysogelos

Timely insights lead to business growth and scientific breakthroughs but require analytical engines that cope with the ever-increasing data processing needs. Analytical engines relied on rapid CPU improvements, yet the end of Dennard scaling stopped the fr ...

EPFL2022

Performance 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 ...

MDPI2022

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