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Accessing Serialized Data Fromats with GPU-Initiated I/O
DescriptionGraphics Processing Units (GPUs) have become essential for scientific data analysis, yet they remain constrained by traditional I/O architectures that rely on data movement initiated by the CPU. While recent GPU-initiated I/O systems like BaM and GeminiFS partially address this limitation, they do not support access to complex serialized data formats such as HDF5, NetCDF, and ADIOS within GPU kernels. These formats are ubiquitous in scientific computing but would require prohibitive reimplementation of existing I/O libraries for direct GPU access.
This work explores a hybrid approach that enables GPU kernels to access serialized data formats through GPU-initiated I/O transfers to a specialized CPU runtime. Our design preserves the rich functionality of existing data format ecosystems while enabling GPU kernels to perform I/O. Our evaluations demonstrate minimal overhead compared to the traditional CPU-initiated approach. As future work, we are exploring reimplementation of I/O libraries to bypass the CPU runtime when possible.
This work explores a hybrid approach that enables GPU kernels to access serialized data formats through GPU-initiated I/O transfers to a specialized CPU runtime. Our design preserves the rich functionality of existing data format ecosystems while enabling GPU kernels to perform I/O. Our evaluations demonstrate minimal overhead compared to the traditional CPU-initiated approach. As future work, we are exploring reimplementation of I/O libraries to bypass the CPU runtime when possible.
Event Type
Workshop
TimeMonday, 17 November 20254:15pm - 4:20pm CST
Location230
Data Analytics
High Performance I/O, Storage, Archive, & File Systems
Storage
Livestreamed
Recorded
TP
W