@article {kukreja2020lcc,
	title = {Lossy Checkpoint Compression in Full Waveform Inversion},
	journal = {Geoscientific Model Development},
	volume = {15},
	number = {9},
	year = {2022},
	note = {(Geoscientific Model Development)},
	month = {5},
	pages = {3815-3829},
	abstract = {This paper proposes a new method that combines check-pointing methods with error-controlled lossy compression for large-scale high-performance Full-Waveform Inversion (FWI), an inverse problem commonly used in geophysical exploration. This combination can significantly reduce data movement, allowing a reduction in run time as well as peak memory. In the Exascale computing era, frequent data transfer (e.g., memory bandwidth, PCIe bandwidth for GPUs, or network) is the performance bottleneck rather than the peak FLOPS of the processing unit. Like many other adjoint-based optimization problems, FWI is costly in terms of the number of floating-point operations, large memory footprint during backpropagation, and data transfer overheads. Past work for adjoint methods has developed checkpointing methods that reduce the peak memory requirements during backpropagation at the cost of additional floating-point computations. Combining this traditional checkpointing with error-controlled lossy compression, we explore the three-way tradeoff between memory, precision, and time to solution. We investigate how approximation errors introduced by lossy compression of the forward solution impact the objective function gradient and final inverted solution. Empirical results from these numerical experiments indicate that high lossy-compression rates (compression factors ranging up to 100) have a relatively minor impact on convergence rates and the quality of the final solution.},
	keywords = {checkpointing, full waveform inversion, GMD, Lossy compression, memory},
	doi = {10.5194/gmd-15-3815-2022},
	url = {https://arxiv.org/pdf/2009.12623.pdf},
	author = {Navjot Kukreja and Jan Hueckelheim and Mathias Louboutin and John Washbourne and Paul H. J. Kelly and Gerard J. Gorman}
}
@article {kukreja2019PASCccd,
	title = {Combining checkpointing and data compression to accelerate adjoint-based optimization problems},
	journal = {Euro-Par 2019: Parallel Processing},
	year = {2019},
	note = {(Euro-Par 2019: Parallel Processing)},
	pages = {87-100},
	publisher = {Springer International Publishing},
	abstract = {Seismic inversion and imaging are adjoint-based optimization problems that processes up to terabytes of data, regularly exceeding the memory capacity of available computers. Data compression is an effective strategy to reduce this memory requirement by a certain factor, particularly if some loss in accuracy is acceptable. A popular alternative is checkpointing, where data is stored at selected points in time, and values at other times are recomputed as needed from the last stored state. This allows arbitrarily large adjoint computations with limited memory, at the cost of additional recomputations. In this paper we combine compression and checkpointing for the first time to compute a realistic seismic inversion. The combination of checkpointing and compression allows larger adjoint computations compared to using only compression, and reduces the recomputation overhead significantly compared to using only checkpointing.},
	keywords = {Adjoint-state, checkpointing, compression, FD, HPC, Inverse problems},
	doi = {10.1007/978-3-030-29400-7_7},
	url = {https://slim.gatech.edu/Publications/Public/Journals/PASC/2019/kukreja2019PASCccd/kukreja2019PASCccd.pdf},
	author = {Navjot Kukreja and Jan H{\"u}ckelheim and Mathias Louboutin and Paul Hovland and Gerard Gorman}
}
