The Paradyn project has two new technical report in the area binary tools and performance evaluation.
Our full list of project publications always can be found at: http://www.paradyn.org/html/publications-by-year.html Comments and feedback on our papers is always welcome! ------------------------------------------------------------------------------ "Exposing Hidden Performance Opportunities in High Performance GPU Applications", Benjamin Welton and Barton P. Miller. Submitted for publication, May 2017. ftp://ftp.cs.wisc.edu/paradyn/papers/welton-unobvious.pdf Abstract: The emergence of leadership class systems with nodes containing many-core accelerators, such as GPUs, has the potential to vastly increase the performance of distributed applications. Exploiting the additional parallelism that manycore accelerators offer is fraught with challenges. Developers and existing performance tools focus on a subset of these challenges, primarily the identification of CPU code that may be suited for many-core parallelization and improving the efficiency of existing many-core code. While this focus has resulted in application performance improvements, a significant amount of untapped performance still remains. Untapped performance opportunities take the form of missed unobvious many-core parallelization opportunities as well as inefficiencies in handling interactions with the accelerator, such as memory copies and synchronization. In this work we address three issues: (1) characterize the missed performance opportunities in many-core applications that are not detected by current performance tools and techniques, (2) design detection methods that can be used by performance tools to identify these missed opportunities, and (3) apply these techniques to five large scale scientific applications (Qball, QBox, Hoomd-blue, LAMMPs, and cuIBM), resulting in a reduction of their execution time by 18% and 87%. ------------------------------------------------------------------------------ "Structured Random Differential Testing of Instruction Decoders", Nathan Jay and Barton P. Miller, submitted for publication, May 2017. ftp://ftp.cs.wisc.edu/paradyn/papers/JayFleece.pdf Abstract: Decoding binary executable files is a critical facility for software analysis, including debugging, performance monitoring, malware detection, cyber forensics, and sandboxing, among other techniques. As a foundational capability, binary decoding must be consistently correct for the techniques that rely on it to be viable. Unfortunately, modern instruction sets are huge and the encodings are complex, so as a result, modern binary decoders are buggy. In this paper, we present a testing methodology that automatically infers structural information for an instruction set and uses the inferred structure to efficiently generate structured-random test cases independent of the instruction set being tested. Our testing methodology includes automatic output verification using differential analysis and reassembly to generate error reports. This testing methodology requires little instruction-set-specific knowledge, allowing rapid testing of decoders for new architectures and extensions to existing ones. We have implemented our testing procedure in a tool name Fleece and used it to test multiple binary decoders (Intel XED, libopcodes, LLVM, Dyninst and Capstone) on multiple architectures (x86, ARM and PowerPC). Our testing efficiently covered thousands of instruction format variations for each instruction set and uncovered decoding bugs in every decoder we tested. _______________________________________________ Dyninst-api mailing list Dyninst-api@cs.wisc.edu https://lists.cs.wisc.edu/mailman/listinfo/dyninst-api
