Fine-Grained Memory Profiling of GPGPU Kernels

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dc.contributor.authorBuelow, Max vonen_US
dc.contributor.authorGuthe, Stefanen_US
dc.contributor.authorFellner, Dieter W.en_US
dc.contributor.editorUmetani, Nobuyukien_US
dc.contributor.editorWojtan, Chrisen_US
dc.contributor.editorVouga, Etienneen_US
dc.date.accessioned2022-10-04T06:39:53Z
dc.date.available2022-10-04T06:39:53Z
dc.date.issued2022
dc.description.abstractMemory performance is a crucial bottleneck in many GPGPU applications, making optimizations for hardware and software mandatory. While hardware vendors already use highly efficient caching architectures, software engineers usually have to organize their data accordingly in order to efficiently make use of these, requiring deep knowledge of the actual hardware. In this paper we present a novel technique for fine-grained memory profiling that simulates the whole pipeline of memory flow and finally accumulates profiling values in a way that the user retains information about the potential region in the GPU program by showing these values separately for each allocation. Our memory simulator turns out to outperform state-of-theart memory models of NVIDIA architectures by a magnitude of 2.4 for the L1 cache and 1.3 for the L2 cache, in terms of accuracy. Additionally, we find our technique of fine grained memory profiling a useful tool for memory optimizations, which we successfully show in case of ray tracing and machine learning applications.en_US
dc.description.number7
dc.description.sectionheadersFast Geometric Computation
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume41
dc.identifier.doi10.1111/cgf.14671
dc.identifier.issn1467-8659
dc.identifier.pages227-235
dc.identifier.pages9 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.14671
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14671
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectCCS Concepts: Hardware → Simulation and emulation; Computing methodologies → Graphics processors; Theory of computation → Program analysis
dc.subjectHardware → Simulation and emulation
dc.subjectComputing methodologies → Graphics processors
dc.subjectTheory of computation → Program analysis
dc.titleFine-Grained Memory Profiling of GPGPU Kernelsen_US
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