Adaptive just-in-time code diversification

We present a method to regenerate diversified code dynamically in a Java bytecode JIT compiler, and to update the diversification frequently during the execution of the program. This way, we can significantly reduce the time frame in which attackers can let a program leak useful address space information and subsequently use the leaked information in memory exploits. A proof of concept implementation is evaluated, showing that even though code is recompiled frequently, we can achieved smaller overheads than the previous state of the art, which generated diversity only once during the whole execution of a program

Performance comparison between Java and JNI for optimal implementation of computational micro-kernels

General purpose CPUs used in high performance computing (HPC) support a vector instruction set and an out-of-order engine dedicated to increase the instruction level parallelism. Hence, related optimizations are currently critical to improve the performance of applications requiring numerical computation. Moreover, the use of a Java run-time environment such as the HotSpot Java Virtual Machine (JVM) in high performance computing is a promising alternative. It benefits from its programming flexibility, productivity and the performance is ensured by the Just-In-Time (JIT) compiler. Though, the JIT compiler suffers from two main drawbacks. First, the JIT is a black box for developers. We have no control over the generated code nor any feedback from its optimization phases like vectorization. Secondly, the time constraint narrows down the degree of optimization compared to static compilers like GCC or LLVM. So, it is compelling to use statically compiled code since it benefits from additional optimization reducing performance bottlenecks. Java enables to call native code from dynamic libraries through the Java Native Interface (JNI). Nevertheless, JNI methods are not inlined and require an additional cost to be invoked compared to Java ones. Therefore, to benefit from better static optimization, this call overhead must be leveraged by the amount of computation performed at each JNI invocation. In this paper we tackle this problem and we propose to do this analysis for a set of micro-kernels. Our goal is to select the most efficient implementation considering the amount of computation defined by the calling context. We also investigate the impact on performance of several different optimization schemes which are vectorization, out-of-order optimization, data alignment, method inlining and the use of native memory for JNI methods.Comment: Part of ADAPT Workshop proceedings, 2015 (arXiv:1412.2347