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.

Serverless computing has become more popular and evolved to support more complex tasks than the original Function as a Service (FaaS) model. The design of serverless systems has advanced to accommodate application demands and offer flexibility. Careful characterization of modern serverless systems and understanding of current gaps are warranted. Publicly available datasets on workloads in select production serverless systems do not fully represent all offerings or capture traces at the required time resolution to identify changes in application-level request-response patterns.

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.

Data-flow analyses like points-to analysis can vastly improve the precision of other analyses, and enable powerful code optimizations. However, whole-program points-to analysis of large Java programs tends to be expensive – both in terms of time and memory. Consequently, many compilers (both static and JIT) and program-analysis tools tend to employ faster – but more conservative – points-to analyses to improve usability. As an alternative to such trading of precision for performance, various techniques have been proposed to perform precise yet expensive fixed-point points-to analyses ahead of time in a static analyzer, store the results, and then transmit them to independent compilation/program-analysis stages that may need them. However, an underlying concern of safety affects all such techniques – can a compiler (or program analysis tool) trust the points-to analysis results generated by another compiler/tool? In this work, we address this issue of trust in the context of Java, while accounting for the issue of performance. We propose ART : Analysis-Results Representation Template – a novel scheme to efficiently and concisely encode results of flow-sensitive, context-insensitive points-to analysis computed by a static analyzer for use in any independent system that may benefit from such a precise points-to analysis. ART also allows for fast regeneration of the encoded sound analysis results in such systems. Our scheme has two components: (i) a producer that can statically perform expensive points-to analysis and encode the same concisely, (ii) a consumer that, on receiving such encoded results (called art work), can regenerate the points-to analysis results encoded by the art work if it is deemed “safe”. The regeneration scheme completely avoids fixed-point computations and thus can help consumers like static analyzers and JIT compilers to obtain precise points-to information without paying a prohibitively high cost. We demonstrate the usage of ART by implementing a producer (in Soot) and two consumers (in Soot and the Eclipse OpenJ9 JIT compiler). We have evaluated our implementation over various benchmarks from the DaCapo and SPECjvm2008 suites. Our results demonstrate that using ART, a consumer can obtain precise flow-sensitive, context-insensitive points-to analysis results in less than (average) 1% of the time taken by a static analyzer to perform the same analysis, with the storage overhead of ART representing a small fraction of the program size (average around 4%).

Just-in-time (JIT) compilers achieve application portability and improved management of large code-bases by abstracting the architecture specific details from programmers. Eclipse OMR and Eclipse OpenJ9 invest extensively in JIT technology to efficiently execute architecture-neutral Java bytecode. OMR is a robust language runtime builder, and OpenJ9 is a managed language runtime that consumes OMR. The targeted domains of OMR and OpenJ9 include AArch64, a 64-bit version of the ARM architecture. AArch64 is a popular member of the embedded computing market, where computing infrastructure resources (e.g., CPU, memory) are constrained. The concept of SIMD (Single Instruction, Multiple Data) instructions primarily evolved to accelerate the performance of multimedia applications such as motion video, real-time physics and graphics where repetitive operations were involved on large arrays of numbers. This paper discusses the steps taken to add SIMD support to OMR for AArch64. The implementation of advanced SIMD and floating-point instructions are also discussed, which cover vectorized mathematical operations, including addition, subtraction, multiplication, and division for supported data-types. We validate our implementation through relevant OMR tril tests, present two microbenchmarks VectorizationMicrobenchmark and Sepia Tone Filter and a set of standard benchmarks, which leverage the OpenJ9 autovectorization process in AArch64. The AArch64 vectorized operations are evaluated against non-vectorized, but similar operations using Eclipse OpenJ9. We demonstrate an improvement of up to four times in execution speed of certain vector arithmetic operations.

In this paper, we describe the techniques that have been implemented in the IBM TestaRossa (TR) just-in-time (JIT) compiler to safely perform aggressive code patching and collect accurate profiles in the context of a Java application employing multiple threads and dynamic class loading and unloading. Previous work in these areas either did not account for the synchronization cost of safety or dynamic class loading/unloading effects in a heavily multithreaded program or did not consider how different patching techniques may be required for different platforms where instruction cache coherence guarantees vary. We evaluate the space and time overhead to make our profiling framework correct, showing that privatizing the profiling variables to achieve correctness impacts execution time only minimally but it can grow the stack frames for profiled methods by less than 15% on average for the SPECjvm98 and SPECjbb2000 benchmarks. Since methods are profiled for only a brief time and the stack frames themselves are not large, we do not consider this growth to be prohibitive. The techniques reported in this paper are implemented in the 1.5.0 release of the IBM Developer Kit for Java targeting 12 different processor-operating system platforms.