Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Demystifying Power and Performance Variations in Gpu Systems Through Microarchitectural Analysis(Comsis Consortium, 2025) Topcu, Burak; Karabacak, Deniz; Oz, IsilGraphics Processing Units (GPUs) serve efficient parallel execution for general-purpose computations at high-performance computing and embedded systems. While performance concerns guide the main optimization efforts, power issues become significant for energy-efficient and sustainable GPU executions. Profilers and simulators report statistics about the target execution; however, they either present only performance metrics in a coarse kernel function level or lack visualization support that can enable microarchitectural performance analysis or performance-power consumption comparison. Evaluating runtime performance and power consumption dynamically across GPU components enables a comprehensive tradeoff analysis for GPU architects and software developers. In this work, we present a novel memory performance and power monitoring tool for GPU programs, GPPRMon, which performs a systematic metric collection and provides useful visualization views to guide power and performance analysis for target executions. Our simulation-based framework dynamically gathers SM and memory-related microarchitectural metrics by monitoring individual instructions and reports dynamic performance and power values. Our interface presents spatial and temporal views of the execution. While the first demonstrates the performance and power metrics across GPU memory components, the latter shows the corresponding information at the instruction granularity in a timeline. We demonstrate performance and power analysis for memory-bound graph applications and resource-critical embedded programs from GPU benchmark suites. Our case studies reveal potential usages of our tool in memory-bound kernel identification, performance bottleneck analysis of a memory-intensive workload, performance-power evaluation of an embedded application, and the impact of input size on the memory structures of an embedded system.Conference Object Evaluating CUDA-Aware Approximate Computing Techniques(CEUR-WS, 2024) Öz, I.Approximate computing techniques offer performance improvements by performing inexact computations. Moreover, CUDA programs written to be executed on GPU devices employ specific features to utilize the parallel computation units of heterogeneous GPU architectures. While generic software-level approximate computing techniques have been applied to heterogeneous CUDA programs, CUDA-specific approaches may introduce promising performance improvements by not corrupting the target computations. In this work, we propose software approximation techniques for CUDA programs: kernel-aware loop perforation, partition-level synchronization, block-level atomic operations, and warp divergence elimination. We perform source code transformations on target benchmark programs by applying our techniques. We evaluate performance improvements by trading off accuracy in our target computations. Our experimental results reveal that CUDA-aware approximation techniques offer significant performance improvements at the expense of acceptable accuracy loss. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).