Array bounds check elimination in the context of deoptimization

AbstractWhenever an array element is accessed, Java virtual machines execute a compare instruction to ensure that the index value is within the valid bounds. This reduces the execution speed of Java programs. Array bounds check elimination identifies situations in which such checks are redundant and can be removed. We present an array bounds check elimination algorithm for the Java HotSpot™ VM based on static analysis in the just-in-time compiler.The algorithm works on an intermediate representation in static single assignment form and maintains conditions for index expressions. It fully removes bounds checks if it can be proven that they never fail. Whenever possible, it moves bounds checks out of loops. The static number of checks remains the same, but a check inside a loop is likely to be executed more often. If such a check fails, the executing program falls back to interpreted mode, avoiding the problem that an exception is thrown at the wrong place.The evaluation shows a speedup near to the theoretical maximum for the scientific SciMark benchmark suite and also significant improvements for some Java Grande benchmarks. The algorithm slightly increases the execution speed for the SPECjvm98 benchmark suite. The evaluation of the DaCapo benchmarks shows that array bounds checks do not have a significant impact on the performance of object-oriented applications

Speculation Without Regret: Reducing Deoptimization Meta-data

Abstract Speculative optimizations are used in most Just In Time (JIT) compilers in order to take advantage of dynamic runtime feedback. These speculative optimizations usually require the compiler to produce meta-data that the Virtual Machine (VM) can use as fallback when a speculation fails. This meta-data can be large and incurs a significant memory overhead since it needs to be stored alongside the machine code for as long as the machine code lives. The design of the Graal compiler leads to many speculations falling back to a similar state and location. In this paper we present deoptimization grouping, an optimization using this property of the Graal compiler to reduce the amount of meta-data that must be stored by the VM without having to modify the VM. We compare our technique with existing meta-data compression techniques from the HotSpot Virtual Machine and study how well they combine. In order to make informed decisions about speculation meta-data, we present an empirical analysis of the origin, impact and usages of this meta-data

Trace Register Allocation Policies: Compile-time vs. Performance Trade-offs

Register allocation is an integral part of compilation, regardless of whether a compiler aims for fast compilation or optimal code quality. State-of-the-art dynamic compilers often use global register allocation approaches such as linear scan. Recent results suggest that non-global trace-based register allocation approaches can compete with global approaches in terms of allocation quality. Instead of processing the whole compilation unit (i.e., method) at once, a trace-based register allocator divides the problem into linear code segments, called traces. In this work, we present a register allocation framework that can exploit the additional flexibility of traces to select different allocation strategies based on the characteristics of a trace. This provides us with fine-grained control over the trade-off between compile time and peak performance in a just-in-time compiler. Our framework features three allocation strategies: a linear-scan-based approach that achieves good code quality, a single-pass bottom-up strategy that aims for short allocation times, and an allocator for trivial traces. To demonstrate the flexibility of the framework, we select 8 allocation policies and show their impact on compile time and peak performance. This approach can reduce allocation time by 7–43 at a peak performance penalty of about 1–11 on average. For systems that do not focus on peak performance, our approach allows to adjust the time spent for register allocation, and therefore the overall compilation time, thus finding the optimal balance between compile time and peak performance according to an application's requirements

Applying Optimizations for Dynamically-typed Languages to Java

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 significantly. With this approach, we get an efficient implementation of Java's generics, avoid changes to the Java language, and maintain backwards compatibility, allowing existing code to benefit from our optimization transparently

Trace-based Register Allocation in a JIT Compiler

State-of-the-art dynamic compilers often use global approaches, like Linear Scan or Graph Coloring, for register allocation. These algorithms consider the complete compilation unit for allocation, which increases the complexity of the implementation (e.g., support for lifetime holes in Linear Scan) and potentially also affects compilation time. We propose a novel non-global algorithm, which splits a compilation unit into traces based on profiling feedback and subsequently performs register allocation within each trace individually. Traces reduce the problem size to a single linear code segment, which simplifies the problem a register allocator needs to solve. Additionally, we can apply different register allocation algorithms to each trace. We show that this non-global approach can achieve results competitive to global register allocation. We present an implementation of Trace Register Allocation based on the Graal VM and show an evaluation for common Java benchmarks. We demonstrate that performance of this non-global approach is within 3% (on AMD64) and 1% (on SPARC) of global Linear Scan register allocation.(VLID)247065

Dynamic code evolution for Java

Dynamic code evolution is a technique to update a program while it is running. In an object-oriented language such as Java, this can be seen as replacing a set of classes by new versions. We modified an existing high-performance virtual machine to allow arbitrary changes to the definition of loaded classes. Besides adding and deleting fields and methods, we also allow any kind of changes to the class and interface hierarchy. Our approach focuses on increasing developer productivity during debugging. Changes can be applied at any point a Java program can be suspended. The evaluation section shows that our modifications to the virtual machine have no negative performance impact on normal program execution. The fast in-place instance update algorithm ensures that the performance characteristics of a change are comparable with performing a full garbage collection run. Standard Java development environments are capable of using the code evolution features of our modified virtual machine, so no additional tools are required