Bringing low-level languages to the JVM: efficient execution of LLVM IR on Truffle

Rigger, Manuel; Grimmer, Matthias; Wimmer, Christian; Würthinger, Thomas; Mössenböck, Hanspeter

doi:10.1145/2998415.2998416

Cited by 31 publications

(18 citation statements)

References 30 publications

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“…Sulong [21,22] is a bytecode interpreter for LLVM IR built on top of Truffle. LLVM IR is the intermediate representation of the LLVM compilation framework [16], which provides front ends for various languages such as C/C++ and Fortran, allowing Sulong to execute these languages via LLVM IR.…”

Section: Sulong and Llvm Irmentioning

confidence: 99%

“…Sulong's execution mode is different from classical ASTbased Truffle language implementations, such as TruffleRuby or GraalPython. It uses a BlockDispatchNode that dispatches the control flow between the individual LLVM IR BasicBlockNodes [21], whereas the high-level control flow is unknown to Sulong (see Figure 1d). This makes Sulong's structure similar to a bytecode interpreter (see Listing 2).…”

Section: Sulong and Llvm Irmentioning

confidence: 99%

“…In the case of loops, the standard version of PE would infinitely inline successor blocks of already inlined blocks. Thus, the partial evaluation had to be adapted [21] to support bytecode interpreters as described below.…”

Section: Partial Evaluation Of Bytecode Interpretersmentioning

confidence: 99%

“…We implemented our approach in Sulong [21,22], an interpreter for LLVM IR, which suffers from long warm-up time [22]. LLVM IR is an unstructured language that does not explicitly represent loops.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Supporting on-stack replacement in unstructured languages by loop reconstruction and extraction

Mosaner¹,

Leopoldseder²,

Rigger

et al. 2019

Proceedings of the 16th ACM SIGPLAN International Conference on Managed Programming Languages and Runtimes

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On-stack replacement (OSR) is a common technique employed by dynamic compilers to reduce program warm-up time. OSR allows switching from interpreted to compiled code during the execution of this code. The main targets are long running loops, which need to be represented explicitly, with dedicated information about condition and body, to be optimized at run time. Bytecode interpreters, however, represent control flow implicitly via unstructured jumps and thus do not exhibit the required high-level loop representation. To enable OSR also for jump-based-often called unstructuredlanguages, we propose the partial reconstruction of loops in order to explicitly represent them in a bytecode interpreter. Besides an outline of the general idea, we implemented our approach in Sulong, a bytecode interpreter for LLVM bitcode, which allows the execution of C/C++. We conducted an evaluation with a set of C benchmarks, which showed speed-ups in warm-up of up to 9x for certain benchmarks. This facilitates execution of programs with long-running loops in rarely called functions, which would yield significant slowdown without OSR. While shown with a prototype implementation, the overall idea of our approach is generalizable for all bytecode interpreters.

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Section: Sulong and Llvm Irmentioning

confidence: 99%

Section: Sulong and Llvm Irmentioning

confidence: 99%

Section: Partial Evaluation Of Bytecode Interpretersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Supporting on-stack replacement in unstructured languages by loop reconstruction and extraction

Mosaner¹,

Leopoldseder²,

Rigger

et al. 2019

Proceedings of the 16th ACM SIGPLAN International Conference on Managed Programming Languages and Runtimes

Self Cite

View full text Add to dashboard Cite

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“…The C⁄C++ code is executed by Sulong [22], an implementation of C⁄C++ on top of Trule. The native interface itself is implemented using Trule's cross-language interoperability mechanism [13,14].…”

Section: Completenessmentioning

confidence: 99%

Applying Optimizations for Dynamically-typed Languages to Java

Grimmer¹,

Marr

Kahlhofer

et al. 2017

Proceedings of the 14th International Conference on Managed Languages and Runtimes

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While Java is a statically-typed language, some of its features make it behave like a dynamically-typed language at run time. This includes Java's boxing of primitive values as well as generics, which rely on type erasure. This paper investigates how runtime technology for dynamically-typed languages such as JavaScript and Python can be used for Java bytecode. Using optimistic optimizations, we specialize bytecode instructions that access references in such a way, that they can handle primitive data directly and also specialize data structures in order to avoid boxing for primitive types. Our evaluation shows that these optimizations can be successfully applied to a statically-typed language such as Java and can also improve performance signiicantly. With this approach, we get an eicient implementation of Java's generics, avoid changes to the Java language, and maintain backwards compatibility, allowing existing code to beneit from our optimization transparently.

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Context-Aware Failure-Oblivious Computing as a Means of Preventing Buffer Overflows

Rigger

Pekarek

Mössenböck

2018

Network and System Security

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In languages like C, buffer overflows are widespread. A common mitigation technique is to use tools that detect them during execution and abort the program to prevent data leakage or the diversion of control flow. However, for server applications, it would be desirable to prevent such errors while maintaining availability of the system. To this end, we present an approach to handling buffer overflows without aborting the program. This approach involves implementing a recovery logic in library functions based on an introspection function that allows querying the size of a buffer. We demonstrate that introspection can be implemented in popular bug-finding and bug-mitigation tools such as LLVM's AddressSanitizer, SoftBound, and Intel-MPX-based bounds checking. We evaluated our approach in a case study of real-world bugs and show that for tools that explicitly track bounds data, introspection results in a low performance overhead.

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Bringing low-level languages to the JVM: efficient execution of LLVM IR on Truffle

Cited by 31 publications

References 30 publications

Supporting on-stack replacement in unstructured languages by loop reconstruction and extraction

Supporting on-stack replacement in unstructured languages by loop reconstruction and extraction

Applying Optimizations for Dynamically-typed Languages to Java

Context-Aware Failure-Oblivious Computing as a Means of Preventing Buffer Overflows

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