3 citations · 6 across the 17 of their papers we have counts for
19 papers
Distributed Datalog at Scale with Worst-Case Optimal Joins
Kunting Qi, Yihao Sun, Kristopher Micinski +2
Scalable logic-based analytics are frequently dominated by cyclic multi-way joins over large relational datasets. State-of-the-art systems evaluate these joins through chains of bi…
When LLM Decompilers Recompile More and Preserve Less
Chang Liu, Edward Raff, Kristopher Micinski
Decompilation recovers high-level source from compiled machine code and serves as a foundation for security tasks such as vulnerability detection and malware analysis. Traditional…
Terascale Query Processing in the Browser: Rethinking GPU Acceleration
Jiaxin Lu, Landon Dyken, Yihao Sun +3
Recursive query computation, central to graph algorithms and relational databases, demands GPU acceleration due to its inherent computational intensity. While substantial prior wor…
ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage
Chang Liu, Noah Fleischmann, Nicolò Altamura +3
Existing binary corpora typically capture only one or two axes of binary variation: they either provide cross-compiler builds without a temporal axis, or CVE labels for single-buil…
Scaling Worst-Case Optimal Datalog to GPUs
Yihao Sun, Kunting Qi, Thomas Gilray +2
Datalog is a declarative logic-programming language used for complex analytic reasoning workloads such as program analysis and graph analytics. Datalog's popularity is due to its u…
Superset Decompilation
Chang Liu, Yihao Sun, Thomas Gilray +1
Decompilation is the task of recovering high-level source from a compiled binary. Conventionally, it is implemented as a series of analyses and rewrites over a single representatio…