Comprehensive application security can only be ensured if all code that it is going to execute is protected: any unprotected code, either from libraries or the application, becomes a potential attack surface. Compilers contain extensive suites of tools to aid in this, but require source availability that is often infeasible. Existing static and dynamic binary rewriting techniques that retrofit for security either lack in code coverage or soundness, or incur very high performance overhead. We present a case for adopting hybrid static-dynamic mechanisms to ensure comprehensive security for binaries, providing sound and practical solutions. We highlight the limitations of existing hybrid tools in their use for security purposes, and provide insights to re-architect them. We provide a framework, Janitizer, that enables sound and comprehensive code coverage for entire applications, presenting hybrid binary implementations for two important classes of security schemes; a memory sanitizer and a control flow integrity scheme. These achieve the coverage and correctness of high-overhead dynamic techniques, while maintaining performance levels of low-coverage static techniques.

Binary instrumentation and rewriting frameworks provide a powerful way of implementing custom analysis and transformation techniques for applications ranging from performance profiling to security monitoring. However, using these frameworks to write even simple analyses and transformations is non-trivial. Developers often need to write framework-specific boilerplate code and work with low-level and complex programming details. This not only results in hundreds (or thousands) of lines of code, but also leaves significant room for error. To address this, we introduce Cinnamon, a domain-specific language designed to write programs for binary profiling and monitoring. Cinnamon's abstractions allow the programmer to focus on implementing their technique in a platform-independent way, without worrying about complex lower-level details. Programmers can use these abstractions to perform analysis and instrumentation at different locations and granularity levels in the binary. The flexibility of Cinnamon also enables its programs to be mapped to static, dynamic or hybrid analysis and instrumentation approaches. As a proof of concept, we target Cinnamon to three different binary frameworks by implementing a custom Cinnamon to C/C++ compiler and integrating the generated code within these frameworks. We further demonstrate the ability of Cinnamon to express a range of profiling and monitoring tools through different use-cases.

Summary As core counts in processors increases, it becomes harder to schedule and distribute work in a timely and scalable manner. This article enhances the scalability of parallel loop schedulers by specializing schedulers for fine‐grain loops. We propose a low‐overhead work distribution mechanism for a static scheduler that uses no atomic operations. We integrate our static scheduler with the Intel OpenMP and Cilkplus parallel task schedulers to build hybrid schedulers. Compiler support enables efficient reductions for Cilk, without changing the programming interface of Cilk reducers. Detailed, quantitative measurements demonstrate that our techniques achieve scalable performance on a 48‐core machine and the scheduling overhead is 43% lower than Intel OpenMP and 12.1× lower than Cilk. We demonstrate consistent performance improvements on a range of HPC and data analytics codes. Performance gains are more important as loops become finer‐grain and thread counts increase. We observe consistently 16%–30% speedup on 48 threads, with a peak of 2.8× speedup.

This work proposes a low-overhead half-barrier pattern to schedule fine-grain parallel loops and considers its integration in the Intel OpenMP and Cilkplus schedulers. Experimental evaluation demonstrates that the scheduling overhead of our techniques is 43% lower than Intel OpenMP and 12.1x lower than Cilk. We observe 22% speedup on 48 threads, with a peak of 2.8x speedup.

This paper presents a novel map-reduce runtime system that is designed for scalability and for composition with other parallel software. We use a modified programming interface that expresses reduction operations over data containers as opposed to key-value pairs. This design choice admits higher efficiency as the programmer can select appropriate data structures. Our runtime targets shared memory systems, which are increasingly capable of performing data analytics on terabyte-sized data sets stored in-memory. Our map-reduce runtime is built over the Cilk programming language and outperforms Phoenix++, by 1.5x-4x for 5 out of 7 map-reduce benchmarks on 48 threads. These results arise from a combination of factors: (i) the reduction of framework overheads, including the elimination of repeated (de-)serialization of key-value pairs; (ii) the use of more appropriate intermediate data structures that reductions over containers support.

One of the main targets of data analytics is unstructured data, which primarily involves textual data. High-performance processing of textual data is non-trivial. We present the HPTA library for high-performance text analytics. The library helps programmers to map textual data to a dense numeric representation, which can be handled more efficiently. HPTA encapsulates three performance optimizations: (i) efficient memory management for textual data, (ii) parallel computation on associative data structures that map text to values and (iii) optimization of the type of associative data structure depending on the program context. We demonstrate that HPTA outperforms popular frameworks for text analytics such as scikit-learn.