Object flattening is a non-trivial optimization that inlines the fields of an object inside its containers. Owing to its direct applicability for immutable objects, Java would soon allow programmers to mark compatible classes as "value types", and Java Virtual Machines (JVMs) to transparently flatten their instances (value objects). Expectations include reduced memory footprint, faster field access, and overall improvement in performance. This paper describes the surprises and challenges we faced while experimenting with value types and object flattening on a real-world JVM, and presents the design of an efficient strategy that selectively flattens profitable value objects, using a novel combination of static and dynamic analyses.Our value-object flattening strategy is based on insights that span the source program, the just-in-time (JIT) compiler employed by the JVM, as well as the underlying hardware. The first insight identifies source-level patterns that favour and oppose value-object flattening. The second insight finds an interesting dependence of object flattening on object scalarization, and estimates the capability of the JIT in avoiding overheads using escape analysis. Finally, the third insight correlates container objects with cache-line size, based on the load semantics of object fields. In order to develop an efficient strategy to flatten potentially profitable objects, we capture these insights in a tool called VFLATTEN that uses a novel combination of static and dynamic analyses and flattens value objects selectively in a production Java runtime.

Static analysis of JavaScript remains notoriously difficult due to the language’s dynamically typed nature, unconventional scoping rules, and pervasive side effects. Unlike mature infrastructures such as LLVM for C/C++ or Soot for Java, comparable frameworks for JavaScript are fragmented and limited in scope. In this paper, we introduce IRIDIUM, a first-of-its-kind framework to statically optimize JavaScript programs. IRIDIUM systematically lowers JavaScript into a structured intermediate representation (called IRI) that models bindings, environments, and control flow explicitly. The resultant expressiveness enables more predictable analyses and transformations, ranging from dataflow tracking to optimization passes to executable code generation for existing runtimes, that are otherwise hindered by the language’s complexity. By bridging the gap between JavaScript’s surface syntax and the requirements of static analysis, IRIDIUM, thus, lays the foundation for a new generation of tools that can reason effectively about modern JavaScript applications.

Just-in-time (JIT) compilers typically sacrifice the precision of program analysis for efficiency, but are capable of performing sophisticated speculative optimizations based on run-time profiles to generate code that is specialized to a given execution. On the contrary, ahead-of-time static compilers can often afford precise flow-sensitive interprocedural analysis, but produce conservative results in scenarios where higher precision could be derived from run-time specialization. In this paper, we propose the first-of-its-kind approach to enrich static analysis with the possibility of speculative optimization during JIT compilation, as well as its usage to perform aggressive stack allocation on a production Java Virtual Machine (JVM). Our approach of combining static analysis with JIT speculation – named CoSSJIT – involves three key contributions. First, we identify the scenarios where a static analysis would make conservative assumptions but a JIT could deliver precision based on run-time speculation. Second, we present the notion of ‘‘speculative conditions’’ and plug them into a static interprocedural dataflow analyzer (whose aim is to identify heap objects that can be allocated on stack), to generate partial results that can be specialized at run-time. Finally, we extend a production JIT compiler to read and enrich static-analysis results with the resolved values of speculative conditions, leading to a practical approach that efficiently combines the best of both worlds. Cherries on the cake: Using CoSSJIT , we obtain 5.7× improvement in stack allocation (translating to performance), while building on a system that ensures functional correctness during JIT compilation.

The runtimes of managed object-oriented languages such as Java allocate objects on the heap, and rely on automatic garbage collection (GC) techniques for freeing up unused objects. Most such runtimes also consist of just-in-time (JIT) compilers that optimize memory access and GC times by employing escape analysis: an object that does not escape (outlive) its allocating method can be allocated on (and freed up with) the stack frame of the corresponding method. However, in order to minimize the time spent in JIT compilation, the scope of such useful analyses is quite limited, thereby restricting their precision significantly. On the contrary, even though it is feasible to perform precise program analyses statically, it is not possible to use their results in a managed runtime without a closed-world assumption. In this paper, we propose a static+dynamic scheme that allows one to harness the results of a precise static escape analysis for allocating objects on stack, while taking care of both soundness and efficiency concerns in the runtime. Our scheme comprises of three key ideas. First, using the results of a statically performed escape analysis, it performs optimistic stack allocation during JIT compilation. Second, it handles the challenges associated with features that may invalidate the optimism, using a novel idea of dynamic heapification. Third, it uses another novel notion of stack ordering, again supported by a static analysis, to reduce the overheads associated with the checks that determine the need for heapification. The static and the runtime components of our approach are implemented in the Soot optimization framework and in the tiered infrastructure of the Eclipse OpenJ9 VM, respectively. To evaluate the benefits, we compare our scheme with the existing escape analysis and find that it succeeds in allocating a much larger number of objects on the stack. Furthermore, the enhanced stack allocation leads to a significant reduction in the number of GC cycles and brings decent performance improvements, especially suited for constrained-memory environments.

Most code is executed more than once. If not entire programs then libraries remain unchanged from one run to the next. Just-in-time compilers expend considerable effort gathering insights about code they compiled many times, and often end up generating the same binary over and over again. We explore how to reuse compiled code across runs of different programs to reduce warm-up costs of dynamic languages. We propose to use speculative contextual dispatch to select versions of functions from an off-line curated code repository . That repository is a persistent database of previously compiled functions indexed by the context under which they were compiled. The repository is curated to remove redundant code and to optimize dispatch. We assess practicality by extending Ř, a compiler for the R language, and evaluating its performance. Our results suggest that the approach improves warmup times while preserving peak performance.

Existing precise context-sensitive heap analyses do not scale well for large OO programs. Further, identifying the right context abstraction becomes quite intriguing as two of the most popular categories of context abstractions (call-site- and object-sensitive) lead to theoretically incomparable precision. In this paper, we address this problem by first doing a detailed comparative study (in terms of precision and efficiency) of the existing approaches, both with and without heap cloning. In addition, we propose novel context abstractions that lead to a new sweet-spot in the arena.

The precision of heap analyses determines the precision of several associated optimizations, and has been a prominent area in compiler research. It has been shown that context-sensitive heap analyses are more precise than the insensitive ones, but their scalability continues to be a cause of concern. Though the value-contexts approach improves the scalability of classical call-string based context-sensitive analyses, it still does not scale well for several popular whole-program heap analyses. In this paper, we propose a three-stage analysis approach that lets us scale complex whole-program value-contexts based heap analyses for large programs, without losing their precision.

Languages like Java and C# follow a two-step process of compilation: static compilation and just-in-time (JIT) compilation. As the time spent in JIT compilation gets added to the execution-time of the application, JIT compilers typically sacrifice the precision of program analyses for efficiency. The alternative of performing the analysis for the whole program statically ignores the analysis of libraries (available only at runtime), and thereby generates imprecise results. To address these issues, in this article, we propose a two-step (static+JIT) analysis framework called precise-yet-efficient (PYE) that helps generate precise analysis-results at runtime at a very low cost. PYE achieves the twin objectives of precision and performance during JIT compilation by using a two-pronged approach: (i) It performs expensive analyses during static compilation, while accounting for the unavailability of the runtime libraries by generating partial results, in terms of conditional values , for the input application. (ii) During JIT compilation, PYE resolves the conditions associated with these values, using the pre-computed conditional values for the libraries, to generate the final results. We have implemented the static and the runtime components of PYE in the Soot optimization framework and the OpenJDK HotSpot Server Compiler (C2), respectively. We demonstrate the usability of PYE by instantiating it to perform two context-, flow-, and field-sensitive heap-based analyses: (i) points-to analysis for null-dereference-check elimination; and (ii) escape analysis for synchronization elimination. We evaluate these instantiations against their corresponding state-of-the-art implementations in C2 over a wide range of benchmarks. The extensive evaluation results show that our strategy works quite well and fulfills both the promises it makes: enhanced precision while maintaining efficiency during JIT compilation.