Buse Yilmaz

Buse Yilmaz
Istinye Universitesi

PhD

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13
Publications
905
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34
Citations

Publications

Publications (13)
Preprint
Full-text available
Sparse triangular solve (SpTRSV) is an extensively studied computational kernel. An important obstacle in parallel SpTRSV implementations is that in some parts of a sparse matrix the computation is serial. By transforming the dependency graph, it is possible to increase the parallelism of the parts that lack it. In this work, we present an approach...
Article
Sparse Triangular Solve (SpTRSV) is an important and extensively used kernel in scientific computing. Parallelism within SpTRSV depends upon matrix sparsity pattern and, in many cases, is non-uniform from one computational step to the next. In cases where the SpTRSV computational steps have contrasting parallelism characteristics some steps are mor...
Preprint
Full-text available
Sparse Triangular Solve (SpTRSV) is an important computational kernel used in the solution of sparse linear algebra systems in many scientific and engineering applications. It is diffcult to parallelize SpTRSV in today's architectures. The limited parallelism due to the dependencies between calculations and the irregular nature of the computations...
Chapter
Sparse triangular solve (SpTRSV) is an important linear algebra kernel, finding extensive uses in numerical and scientific computing. The parallel implementation of SpTRSV is a challenging task due to the sequential nature of the steps involved. This makes it, in many cases, one of the most time-consuming operations in an application. Many approach...
Preprint
Sparse triangular solve (SpTRSV) is an important linear algebra kernel, finding extensive uses in numerical and scientific computing. The parallel implementation of SpTRSV is a challenging task due to the sequential nature of the steps involved. This makes it, in many cases, one of the most time-consuming operations in an application. Many approach...
Conference Paper
Sparse triangular solve (SpTRSV) is an important scientific kernel used in several applications such as preconditioners for Krylov methods. Parallelizing SpTRSV on multi-core systems is challenging since it exhibits limited parallelism due to computational dependencies and introduces high parallelization overhead due to finegrained and unbalanced n...
Conference Paper
Today's computer systems have become increasingly heterogeneous. Data centers integrate accelerators, CPUs with heterogeneous cores and with various ISAs which exhibit different performance and power characteristics. Mobile phones, following a similar trend, switch between fast and energy-efficient cores. Process migration is an important technique...
Article
Runtime specialization is used for optimizing programs based on partial information available only at run-time. In this paper we apply autotuning on runtime specialization of Sparse Matrix-Vector Multiplication to predict a best specialization method among several. In 91-96% of the predictions, either the best or the second best method is chosen. P...
Article
Full-text available
Runtime specialization optimizes programs based on partial information available only at run time. It is applicable when some input data is used repeatedly while other input data varies. This technique has the potential of generating highly efficient codes. In this paper, we explore the potential for obtaining speedups for sparse matrix-dense vecto...
Conference Paper
Full-text available
A compiler performs several passes over a program during the semantic analysis and optimization phases before emitting executable code. The time required to do program analysis can be substantially reduced by performing it in parallel. Lee, Ryder and Fiuczynski's Region Analysis is a method that partitions the flow graph of a program to enable bett...
Conference Paper
Full-text available
Hybridization of local search algorithms yield promising algorithms for combinatorial optimization problems such as Graph Coloring Problem (GCP). This paper presents a new meta-heuristic Simulated Annealing with Backtracking (SABT) and shows the effect of hill climber and tabu search on SABT for solving GCP. The algorithm proposed merges the power...
Conference Paper
Full-text available
Linear Linkage Encoding (LLE) is a powerful encoding scheme utilized when genetic algorithms (GAs) are applied to grouping problems. It discards the redundancy of other traditional encoding schemes. However, some genetic operators are quite costly in terms of computational time when LLE is utilized. In this study, two supplementary encoding schemes...

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Projects (2)
Project
The main objectives of the graph transformation and specialized code generation framework that will be developed are: 1. Using graph transformation, to make the sparsity pattern more uniform by increasing the parallelism of the matrix portions where it is scarce 2. To reduce the need for synchronization points, 3. To generate specialized code for SpTRSV on CPUs, unlocking domain-specific optimizations.
Project
Optimizing performance benefits on Heterogeneous-ISA platforms using compiler-based analysis and path profiling & performance prediciton