Binary code analysis is widely used to assess a programs correctness, performance, and provenance. Binary analysis applications often construct control flow graphs, analyze data flow, and use debugging information to understand how machine code relates to source lines, inlined functions, and data types. To date, binary analysis has been single-threaded, which is too slow for applications such as performance analysis and software forensics, where it is becoming common to analyze binaries that are gigabytes in size and in large batches that contain thousands of binaries. This paper describes our design and implementation for accelerating the task of constructing control flow graphs (CFGs) from binaries with multithreading. Existing research focuses on addressing challenging code constructs encountered during constructing CFGs, including functions sharing code, jump table analysis, non-returning functions, and tail calls. However, existing analyses do not consider the complex interactions between concurrent analysis of shared code, making it difficult to extend existing serial algorithms to be parallel. A systematic methodology to guide the design of parallel algorithms is essential. We abstract the task of constructing CFGs as repeated applications of several core CFG operations regarding to creating functions, basic blocks, and edges. We then derive properties among CFG operations, including operation dependency, commutativity, monotonicity. These operation properties guide our design of a new parallel analysis for constructing CFGs. We achieved as much as 25$times$ speedup for constructing CFGs on 64 hardware threads. Binary analysis applications are significantly accelerated with the new parallel analysis: we achieve 8$times$ for a performance analysis tool and 7$times$ for a software forensic tool with 16 hardware threads.
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