Researcher:
Yılmaz, Buse

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Buse

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Yılmaz

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Yılmaz, Buse

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2019 - 20203

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    Program analysis for process migration
    (Assoc Computing Machinery, 2019) Department of Computer Engineering; Department of Computer Engineering; Department of Computer Engineering; Yılmaz, Buse; Turimbetov, İlyas; Erten, Didem Unat; Researcher; PhD Student; Faculty Member; Department of Computer Engineering; College of Engineering; College of Engineering; College of Engineering; N/A; N/A; 219274
    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 to leverage such specialization and heterogeneity. In this work, we target process migration enabled OS-capable heterogeneous platforms and address how to obtain better performance by program analysis: we address the challenge of defining migration points at which the program state is the same across machines and whether these will match phase changes, changes in the program behavior. Our tool-chain employs both static and dynamic analysis to compensate for disadvantages of both techniques to reduce the analyses overhead. Six out of ten benchmarks from different benchmark suites benefit from migration and the migration cost is compensated by the performance gained from migrating.
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    Adaptive level binning: a new algorithm for solving sparse triangular systems
    (Information Processing Society of Japan (IPSJ), 2020) Department of Computer Engineering; Department of Computer Engineering; N/A; Department of Computer Engineering; Erten, Didem Unat; Yılmaz, Buse; Ahmad, Najeeb; Sipahioğlu, Buğra; Faculty Member; Researcher; PhD Student; Undergraduate Student; Department of Computer Engineering; College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; College of Engineering; 219274; N/A; N/A; N/A
    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 nature of workloads. We propose a novel method, named Adaptive Level Binning (ALB), that addresses these challenges by eliminating redundant synchronization points and adapting the work granularity with an efficient load balancing strategy. Similar to the commonly used level-set methods for solving SpTRSV, ALB constructs level-sets of rows, where each level can be computed in parallel. Differently, ALB bins rows to levels adaptively and reduces redundant dependencies between rows. On an Intel® Xeon® Gold 6148 processor and NVIDIA® Tesla V100 GPU, ALB obtains 1.83x speedup on average and up to 5.28x speedup over Intel MKL and, over NVIDIA cuSPARSE, an average speedup of 2.80x and a maximum speedup of 39.40x for 29 matrices selected from Suite Sparse Matrix Collection.
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    A prediction framework for fast sparse triangular solves
    (Springer International Publishing Ag, 2020) N/A; N/A; Department of Computer Engineering; Ahmad, Najeeb; Yılmaz, Buse; Erten, Didem Unat; PhD Student; N/A; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; N/A; College of Engineering; N/A; N/A; 219274
    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 approaches for efficient SpTRSV on CPU and GPU systems have been proposed in the literature. However, no single implementation or platform (CPU or GPU) gives the fastest solution for all input sparse matrices. In this work, we propose a machine learning-based framework to predict the SpTRSV implementation giving the fastest execution time for a given sparse matrix based on its structural features. The framework is tested with six SpTRSV implementations on a state-of-the-art CPU-GPU machine (Intel Xeon Gold CPU, NVIDIA V100 GPU). Experimental results, with 998 matrices taken from the SuiteSparse Matrix Collection, show the classifier prediction accuracy of 87% for the fastest SpTRSV algorithm for a given input matrix. Predicted SpTRSV implementations achieve average speedups (harmonic mean) in the range of 1.4-2.7x against the six SpTRSV implementations used in the evaluation.