System emulation and firmware re-hosting have become popular techniques to answer various security and performance related questions, such as determining whether a firmware contain security vulnerabilities or meet timing requirements when run on a specific hardware platform. While this motivation for emulation and binary analysis has previously been explored and reported, starting to either work or research in the field is difficult. To this end, we provide a comprehensive guide for the practitioner or system emulation researcher. We layout common challenges faced during firmware re-hosting, explaining successive steps and surveying common tools used to overcome these challenges. We provide classification techniques on five different axes, including emulator methods, system type, fidelity, emulator purpose, and control. These classifications and comparison criteria enable the practitioner to determine the appropriate tool for emulation. We use our classifications to categorize popular works in the field and present 28 common challenges faced when creating, emulating, and analyzing a system from obtaining firmwares to post emulation analysis.

Remarkable advancements in high-throughput gene sequencing technologies have led to an exponential growth in the number of sequenced genomes. However, unavailability of highly parallel and scalable de novo assembly algorithms have hindered biologists attempting to swiftly assemble high-quality complex genomes. Popular de Bruijn graph assemblers, such as IDBA-UD, generate high-quality assemblies by iterating over a set of k-values used in the construction of de Bruijn graphs (DBG). However, this process of sequentially iterating from small to large k-values slows down the process of assembly. In this paper, we propose ScalaDBG, which metamorphoses this sequential process, building DBGs for each distinct k-value in parallel. We develop an innovative mechanism to “patch” a higher k-valued graph with contigs generated from a lower k-valued graph. Moreover, ScalaDBG leverages multi-level parallelism, by both scaling up on all cores of a node, and scaling out to multiple nodes simultaneously. We demonstrate that ScalaDBG completes assembling the genome faster than IDBA-UD, but with similar accuracy on a variety of datasets (6.8X faster for one of the most complex genome in our dataset).

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Breakthroughs in gene sequencing technologies have led to an exponential increase in the amount of genomic data. Efficient tools to rapidly process such large quantities of data are critical in the study of gene functions, diseases, evolution, and population variation. These tools are designed in an ad-hoc manner, and require extensive programmer effort to develop and optimize them. Often, such tools are written with the currently available data sizes in mind, and soon start to under perform due to the exponential growth in data. Furthermore, to obtain high-performance, these tools require parallel implementations, adding to the development complexity. This paper makes an observation that most such tools contain a recurring set of software modules, or kernels. The availability of efficient implementations of such kernels can improve programmer productivity, and provide effective scalability with growing data. To achieve this goal, the paper presents a domain-specific language, called Sarvavid, which provides these kernels as language constructs. Sarvavid comes with a compiler that performs domain-specific optimizations, which are beyond the scope of libraries and generic compilers. Furthermore, Sarvavid inherently supports exploitation of parallelism across multiple nodes. To demonstrate the efficacy of Sarvavid, we implement five well-known genomics applications-BLAST, MUMmer, E-MEM, SPAdes, and SGA-using Sarvavid. Our versions of BLAST, MUMmer, and E-MEM show a speedup of 2.4X, 2.5X, and 2.1X respectively compared to hand-optimized implementations when run on a single node, while SPAdes and SGA show the same performance as handwritten code. Moreover, Sarvavid applications scale to 1024 cores using a Hadoop backend.