A Case for Compressing Traces with BDDs

Instruction-level traces are widely used for program and hardware analysis. However, program traces for just a few seconds of execution are enormous, up to several terabytes in size, uncompressed. Specialized compression can shrink traces to a few gigabytes, but trace analyzers typically stream the decompressed trace through the analysis engine. Thus, the complexity of analysis depends on the decompressed trace size (even though the decompressed trace is never stored to disk). This makes many global or interactive analyses infeasible. This paper presents a method to compress program traces using binary decision diagrams (BDDs). BDDs intrinsically support operations common to many desirable program analyses and these analyses operate directly on the BDD. Thus, they are often polynomial in the size of the compressed representation. The paper presents mechanisms to represent a variety of trace data using BDDs and shows that BDDs can store, in 1 GB of RAM, the entire data-dependence graph of traces with over 1 billion instructions. This allows rapid computation of global analyses such as heap-object liveness and dynamic slicing