Program execution traces are frequently used in industry and academia. Yet, most trace-compression algorithms have to be re-implemented every time the trace format is changed, which takes time, is error prone, and often results in inefficient solutions. This paper describes and evaluates TCgen, a too that automatically generates portable, customized, high-performance trace compressors. All the user has to do is provide a description of the trace format and select one or more predictors to compress the fields in the trace records. TCgen translates this specification into C source code and optimizes it for the specified trace format and predictor algorithms. On average, the generated code is faster and compresses better than the six other compression algorithms we have tested. For example, a comparison with SBC, one of the best trace-compression algorithms in the current literature, shows that TCgen's synthesized code compresses SPECcpu2000 address traces 23% more, decompresses them 24% faster, and compresses them 1029% faster.

Execution traces, such as are used to study and analyze program behavior, are often so large that they need to be stored in compressed form. This paper describes the design and implementation of four value prediction-based compression (VPC) algorithms for traces that record the PC as well as other information about executed instructions. VPC1 directly compresses traces using value predictors, VPC2 adds a second compression stage, and VPC3 utilizes value predictors to convert traces into streams that can be compressed better and more quickly than the original traces. VPC4 introduces further algorithmic enhancements and is automatically synthesized. Of the 55 SPECcpu2000 traces we evaluate, VPC4 compresses 36 better, decompresses 26 faster, and compresses 53 faster than BZIP2, MACHE, PDATS II, SBC, and SEQUITUR. It delivers the highest geometric-mean compression rate, decompression speed, and compression speed because of the predictors' simplicity and their ability to exploit local value locality. Most other compression algorithms can only exploit global value locality.