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In this work, we present an efficient Processing in MRAM-Accelerated De Bruijn Graph-based DNA Assembly platform, named PANDA, based on an optimized and hardware-friendly genome assembly algorithm. PANDA is able to assemble large-scale DNA sequence datasets from all-pair overlaps. We first design a PANDA platform that exploits MRAM as computational memory and converts it to a potent processing unit for genome assembly. PANDA can not only execute efficient bulk bit-wise X(N)OR-based comparison/addition operations heavily required for the genome assembly task but also a full set of 2-/3-input logic operations inside the MRAM chip. We then develop a highly parallel and step-by-step hardware-friendly DNA assembly algorithm for PANDA that only requires the developed in-memory logic operations. The platform is then configured with a novel data partitioning and mapping technique that provides local storage and processing to utilize the algorithm level’s parallelism fully. The cross-layer simulation results demonstrate that PANDA reduces the run time and power by a factor of 18 and 11, respectively, compared with CPU. Moreover, speed-ups of up to 2.5 to 10× can be obtained over other recent processing in-memory platforms to perform the same task, like STT-MRAM, ReRAM, and DRAM.
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PyPANDA: Taming the PANDAmonium of Whole System Dynamic Analysis
When working with real world programs, dynamic analyses often must be run on a whole-system instead of just a single binary. Existing whole-system dynamic analysis platforms generally require analyses to be written in compiled languages, a suboptimal choice for many iterative analysis tasks. Furthermore, these platforms leave analysts with a split view between the behavior of the system under analysis and the analysis itself---in particular the system being analyzed must commonly be controlled manually while analysis scripts are run. To improve this process, we designed and implemented PyPANDA, a Python interface to the PANDA dynamic analysis platform. PyPANDA unifies the gap between guest virtual machines behavior and analysis tasks; enables painless integrations with other program analysis tools; and greatly lowers the barrier of entry to whole-system dynamic analysis. The capabilities of PyPANDA are demonstrated by using it to dynamically evaluate the accuracy of three binary analysis frameworks, track heap allocations across multiple processes, and synchronize state between PANDA and a binary analysis platform. Significant challenges were overcome to integrate a scripting language into PANDA with minimal performance impact.
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- Award ID(s):
- 1657199
- PAR ID:
- 10251208
- Date Published:
- Journal Name:
- NDSS Binary Analysis Research Workshop
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Binary program dependence analysis determines dependence between instructions and hence is important for many applications that have to deal with executables without any symbol information. A key challenge is to identify if multiple memory read/write instructions access the same memory location. The state-of-the-art solution is the value set analysis (VSA) that uses abstract interpretation to determine the set of addresses that are possibly accessed by memory instructions. However, VSA is conservative and hence leads to a large number of bogus dependences and then substantial false positives in downstream analyses such as malware behavior analysis. Furthermore, existing public VSA implementations have difficulty scaling to complex binaries. In this paper, we propose a new binary dependence analysis called BDA enabled by a randomized abstract interpretation technique. It features a novel whole program path sampling algorithm that is not biased by path length, and a per-path abstract interpretation avoiding precision loss caused by merging paths in traditional analyses. It also provides probabilistic guarantees. Our evaluation on SPECINT2000 programs shows that it can handle complex binaries such as gcc whereas VSA implementations from the-state-of-art platforms have difficulty producing results for many SPEC binaries. In addition, the dependences reported by BDA are 75 and 6 times smaller than Alto, a scalable binary dependence analysis tool, and VSA, respectively, with only 0.19% of true dependences observed during dynamic execution missed (by BDA). Applying BDA to call graph generation and malware analysis shows that BDA substantially supersedes the commercial tool IDA in recovering indirect call targets and outperforms a state-of-the-art malware analysis tool Cuckoo by disclosing 3 times more hidden payloads.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.14722/bar.2021.23001
