What is she best known for?
The fields of study she is best known for:
- Operating system
- Programming language
- Parallel computing
Katherine Yelick spends much of her time researching Parallel computing, Programming language, Unified Parallel C, Sparse matrix and Cache.
Her work on Multi-core processor as part of general Parallel computing study is frequently linked to Context, therefore connecting diverse disciplines of science.
Her Unified Parallel C research is multidisciplinary, incorporating perspectives in Compiler, Shared memory and Distributed shared memory.
The Sparse matrix-vector multiplication research Katherine Yelick does as part of her general Sparse matrix study is frequently linked to other disciplines of science, such as Kernel, therefore creating a link between diverse domains of science.
Her Cache research is multidisciplinary, relying on both Kernel and Computational science.
Her Programming paradigm research includes themes of Data type and Supercomputer.
Her most cited work include:
- The Landscape of Parallel Computing Research: A View from Berkeley (1818 citations)
- The International Exascale Software Project roadmap (580 citations)
- A case for intelligent RAM (527 citations)
What are the main themes of her work throughout her whole career to date?
Her primary areas of study are Parallel computing, Programming language, Partitioned global address space, Distributed computing and Compiler.
Her study in Parallel computing is interdisciplinary in nature, drawing from both Scalability, Programming paradigm and Sparse matrix.
Katherine Yelick focuses mostly in the field of Programming paradigm, narrowing it down to topics relating to Supercomputer and, in certain cases, Benchmark.
Her work in Programming language addresses subjects such as SPMD, which are connected to disciplines such as Model of computation.
Her study looks at the relationship between Partitioned global address space and topics such as Shared memory, which overlap with Distributed shared memory and Sequential consistency.
Her Distributed computing research includes elements of Software and Data structure.
She most often published in these fields:
- Parallel computing (54.48%)
- Programming language (16.21%)
- Partitioned global address space (15.17%)
What were the highlights of her more recent work (between 2015-2021)?
- Parallel computing (54.48%)
- Genomics (4.83%)
- Scalability (12.41%)
In recent papers she was focusing on the following fields of study:
Katherine Yelick focuses on Parallel computing, Genomics, Scalability, Parallel algorithm and Supercomputer.
Katherine Yelick has included themes like Process, Graph and Overhead in her Parallel computing study.
Her studies deal with areas such as Hash table and Distributed computing as well as Scalability.
Her research integrates issues of Sparse matrix, Distributed memory and Pipeline in her study of Parallel algorithm.
Her work deals with themes such as Sparse approximation, Iterative reconstruction and Matrix multiplication, which intersect with Sparse matrix.
In general Supercomputer, her work in Cray XK7 is often linked to Scale linking many areas of study.
Between 2015 and 2021, her most popular works were:
- Communication-Avoiding Parallel Sparse-Dense Matrix-Matrix Multiplication (28 citations)
- Exascale applications: skin in the game. (19 citations)
- Extreme scale de novo metagenome assembly (16 citations)
In her most recent research, the most cited papers focused on:
- Operating system
- Programming language
- Algorithm
Genomics, Parallel computing, Sequence assembly, Parallel algorithm and Data science are her primary areas of study.
Katherine Yelick has researched Parallel computing in several fields, including Scalability and Sparse approximation.
Her Scalability research is multidisciplinary, incorporating elements of Workload, Xeon Phi and Distributed memory.
Katherine Yelick combines subjects such as Pipeline, Supercomputer, Matrix multiplication and Sparse matrix with her study of Parallel algorithm.
Her work in the fields of Supercomputer, such as Exascale computing, overlaps with other areas such as National security, Skin in the game, Exploit and Metaphor.
Her studies in Data science integrate themes in fields like Profiling, Cluster analysis, Sorting, Hash function and Asynchronous communication.
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