Combining Checkpointing and Data Compression to Accelerate Adjoint-Based Optimization Problems

TL;DR

This paper introduces a novel approach combining data compression and checkpointing to efficiently perform large-scale adjoint-based seismic inversion, reducing memory use and computational overhead.

Contribution

First integration of checkpointing and data compression for seismic inversion, enabling larger problems with less recomputation and memory.

Findings

Combined method allows larger adjoint computations.

Reduces recomputation overhead significantly.

Enables realistic seismic inversion with limited resources.

Abstract

Seismic inversion and imaging are 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 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.