Combining checkpointing and data compression for large scale seismic inversion
Seismic inversion is a class of adjoint-based optimization problems that process 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 talk I discuss the combination of compression and checkpointing 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.
