What is he best known for?
The fields of study he is best known for:
- Operating system
- Programming language
- Central processing unit
His scientific interests lie mostly in Parallel computing, Programming paradigm, Scheduling, Distributed computing and Programmer.
Eduard Ayguadé has researched Parallel computing in several fields, including Programming language, Compiler and Computer architecture.
Eduard Ayguadé combines subjects such as Superscalar, Theory of computation, Multi-core processor and Implementation with his study of Programming paradigm.
His Scheduling research includes elements of Schedule and Very long instruction word.
His Distributed computing study combines topics from a wide range of disciplines, such as Multiplayer game, Workload, Task, Source code and Proof of concept.
His work carried out in the field of Programmer brings together such families of science as CUDA and Software portability.
His most cited work include:
- OmpSs: A proposal for programming heterogeneous multi-core architectures (441 citations)
- The Design of OpenMP Tasks (308 citations)
- Barcelona OpenMP Tasks Suite: A Set of Benchmarks Targeting the Exploitation of Task Parallelism in OpenMP (221 citations)
What are the main themes of his work throughout his whole career to date?
Eduard Ayguadé spends much of his time researching Parallel computing, Programming paradigm, Distributed computing, Compiler and Scheduling.
His Parallel computing research incorporates elements of Programming language, Thread and Computer architecture.
His Programming paradigm research is multidisciplinary, incorporating elements of Runtime system, Programmer, CUDA, Task and Multi-core processor.
His studies in Distributed computing integrate themes in fields like Workload, Scalability and Supercomputer.
The subject of his Compiler research is within the realm of Operating system.
Eduard Ayguadé studies Scheduling, focusing on Software pipelining in particular.
He most often published in these fields:
- Parallel computing (50.33%)
- Programming paradigm (31.02%)
- Distributed computing (23.64%)
What were the highlights of his more recent work (between 2015-2021)?
- Parallel computing (50.33%)
- Programming paradigm (31.02%)
- Distributed computing (23.64%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Parallel computing, Programming paradigm, Distributed computing, Scalability and Artificial intelligence.
Eduard Ayguadé combines subjects such as Synchronization and Set with his study of Parallel computing.
His study in Programming paradigm is interdisciplinary in nature, drawing from both Thread, Compiler, Runtime system, Embedded system and Task.
His Compiler research includes themes of Software portability and Serialization.
His work deals with themes such as Scheduling, Supercomputer, Server and System software, which intersect with Distributed computing.
The Scalability study combines topics in areas such as Algorithm and Analytics.
Between 2015 and 2021, his most popular works were:
- The mont-blanc prototype: an alternative approach for HPC systems (50 citations)
- On the Behavior of Convolutional Nets for Feature Extraction (41 citations)
- CUDAlign 4.0: Incremental Speculative Traceback for Exact Chromosome-Wide Alignment in GPU Clusters (32 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Central processing unit
- Programming language
His main research concerns Parallel computing, Distributed computing, Scalability, Programming paradigm and Artificial intelligence.
The various areas that Eduard Ayguadé examines in his Parallel computing study include Thread and Decomposition.
His Thread research focuses on Instruction set and how it connects with Workload and Programmer.
His Distributed computing study integrates concerns from other disciplines, such as Exploit, Complex system, Scheduling and System software.
His research integrates issues of Algorithm, Dynamic programming and Supercomputer in his study of Scalability.
His Programming paradigm research is multidisciplinary, incorporating perspectives in Instrumentation, Task, Set, Embedded system and Software.
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