MPLR 2021: Proceedings of the 18th ACM SIGPLAN International Conference on Managed Programming Languages and Runtimes

Multi-tenant Software-as-a-Service (SaaS) providers allow tenants to customize the application at different levels. When the customization involves tenant custom code and a single application instance is shared among multiple tenants, the issue of tenant isolation becomes critical. In common practice, tenant isolation, which amounts to protection of tenants against any interference and disturbance from each other, is performed by isolating tenant custom code in either a dedicated Virtual Machine (VM) or a dedicated container.

However, tenant isolation can be enforced at the higher level of threads rather than OS processes. The main advantage of this alternative approach is that it significantly increases tenant accommodation capacity (the number of tenants that can be hosted on a single node). However, achieving this benefit raises a number of non-trivial challenges, most notably the need for access control over the memory space shared between the custom code of multiple tenants.

In this paper, we present a solution for protecting the shared memory space of the Java Virtual Machine (JVM) demarcated by the static fields of the java.base module. The solution is based on systematic analysis of the java.base module. As a result of the analysis, the set of shared classes is reduced to a minimal subset of the java.base module and it is shown that the static fields of the minimal subset can be protected using the Java platform security. A multi-tenant class loading mechanism is also provided for loading a tenant-specific runtime instance of classes not included in the minimal subset.

The proposed solution is implemented on top of a customized OpenJDK 11 and validated by means of 18 validation scenarios. The evaluation results presented in this paper show that achieving a memory footprint reduction ranging between 32% and 97% requires only 32 CLOC in OpenJDK source and denial of only 9 distinct permissions for tenants without any significant performance overhead for a wide range of application domains.

Testing and debugging a Virtual Machine is a laborious task without the proper tooling. This is particularly true for VMs with JIT compilation and dynamic code patching for techniques such as inline caching. In addition, this situation is getting worse when the VM builds and runs on multiple target architectures.

In this paper, we report on several lessons we learned while testing the Pharo VM, particularly during the port of its Cogit JIT compiler to the AArch64 architecture. The Pharo VM presented already a simulation environment that is very handy to simulate full executions and live-develop the VM. However, this full simulation environment makes it difficult to reproduce short and simple testing scenarios. We extended the pre-existing simulation environment with a testing infrastructure and a methodology that allow us to have fine-grained control of testing scenarios, making tests small, fast, reproducible, and cross-ISA.

We report on how this testing infrastructure allowed us to cope with two different development scenarios: (1) porting the Cogit JIT compiler to AArch64 without early access to real hardware and (2) debugging memory corruptions due to GC bugs.

Programming microcontrollers involves low level interfacing with hardware and peripherals that are concurrent and reactive. Such programs are typically written in a mixture of C and assembly using concurrent language extensions (like FreeRTOS tasks and semaphores), resulting in unsafe, callback-driven, error-prone and difficult-to-maintain code.

We address this challenge by introducing SenseVM - a bytecode-interpreted virtual machine that provides a message passing based higher-order concurrency model, originally introduced by Reppy, for microcontroller programming. This model treats synchronous operations as first-class values (called Events) akin to the treatment of first-class functions in functional languages. This primarily allows the programmer to compose and tailor their own concurrency abstractions and, additionally, abstracts away unsafe memory operations, common in shared-memory concurrency models, thereby making microcontroller programs safer, composable and easier-to-maintain.

Our VM is made portable via a low-level bridge interface, built atop the embedded OS - Zephyr. The bridge is implemented by all drivers and designed such that programming in response to a software message or a hardware interrupt remains uniform and indistinguishable. In this paper we demonstrate the features of our VM through an example, written in a Caml-like functional language, running on the nRF52840 and STM32F4 microcontrollers.

Scala 3 provides a metaprogramming interface that represents the abstract syntax tree definitions using algebraic data types. To allow the compiler to freely evolve without breaking the metaprogramming interface, we present virtual algebraic data types (or Virtual ADTs) -- a programming pattern, which allows programmers to describe mutually recursive hierarchies of types without coupling to a particular runtime representation.

The Collections framework is an essential utility in virtually every Java application. It offers a set of fundamental data structures that exploit Java Generics and the Object type in order to enable a high degree of reusability. Upon instantiation, Collections are parametrized by the type they are meant to store. However, at compile-time, due to type erasure, this type gets replaced by Object, forcing the data structures to manipulate references of type Object (the root of the Java type system). In the bytecode, the compiler transparently adds type checking instructions to ensure type safety, and generates bridge methods to enable the polymorphic behavior of parametrized classes. This approach can introduce non-trivial runtime overheads when applications extensively manipulate Collections.

We propose the Java Collections Specializer (JCS), a tool we have developed to deliver truly specialized Collections. JCS can generate ArrayLists, ConcurrentHashMaps and HashMaps with true type specialization that incur no performance penalties due to bridge methods or type checking instructions. JCS offers the possibility to easily extend its use to other Collection data structures. Since the specialized data structures extend and inherit from the generic counterpart's superclasses and interfaces, the specialized versions can be used in most places where generic versions are employed. The programmer uses JCS to generate specializations ahead of time. These are generated under the java.util package, and need only be added to the class path and integrated into the application logic. We show that the specialized data structures can improve the runtime performance of data intensive workloads by up to 14% for read use-cases and 42% for write use-cases.

Programmers can use various data types when developing software. However, if the program is compiled to machine code, most of this type information is lost. If analysis of a compiled program is necessary, the lost data types have to be recovered again, to make the code understandable. Existing approaches for the type recovery problem require detailed knowledge about the CPU architecture in question. An architecture-agnostic approach is missing so far.

This work focuses on a truly architecture-agnostic type recovery algorithm, implemented in a dynamic analysis system. It can recover data types using minimal knowledge about the CPU architecture, therefore, making it easy to support many different CPU architectures in the analysis system.

Virtual machine performance tuning for a given application is an arduous and challenging task. For example, parametrizing the behaviour of the JIT compiler machine code caches affects the overall performance of applications while being rather obscure for final users not knowledgeable about VM internals. Moreover, VM components are often heavily coupled and changes in some parameters may affect several seemingly unrelated components and may have unclear performance impacts. Therefore, choosing the best parametrization requires to have precise information.

In this paper, we present Vicoca, a tool that allows VM users and developers to obtain detailed information about the behaviour of the code caches and their interactions with other virtual machine components. We present a complex optimization problem due to the heavy interaction of components in the Pharo VM, and we explain it using Vicoca. The information produced by the tool allows developers to produce an optimized configuration for the VM. Vicoca is based on event recording that are manipulated during off-line analysis. Vicoca not only allows us to understand this given problem, but it opens the door to future work such as automatic detection of application characteristics, identification of performance issues, and automatic hinting.

Dynamic taint analysis (DTA) is a popular program analysis technique with applications to diverse fields such as software vulnerability detection and reverse engineering. It consists of marking sensitive data as tainted and tracking its propagation at runtime. While DTA has been implemented on top of many different analysis platforms, these implementations generally incur significant slowdown from taint propagation. Since a purely dynamic analysis cannot predict which instructions will operate on tainted values at runtime, programs have to be fully instrumented for taint propagation even when they never actually observe tainted values. We propose leveraging speculative optimizations to reduce slowdown on the peak performance of programs instrumented for DTA on a managed runtime capable of dynamic compilation.

In this paper, we investigate how speculative optimizations can reduce the peak performance impact of taint propagation on programs executed on a managed runtime. We also explain how a managed runtime can implement DTA to be amenable to such optimizations. We implemented our ideas in TruffleTaint, a DTA platform which supports both dynamic languages like JavaScript and languages like C and C++ which are typically compiled statically. We evaluated TruffleTaint on several benchmarks from the popular Computer Language Benchmarks Game and SPECint 2017 benchmark suites. Our evaluation shows that TruffleTaint is often able to avoid slowdown entirely when programs do not operate on tainted data, and that it exhibits slowdown of on average ∼2.10x and up to ∼5.52x when they do, which is comparable to state-of-the-art taint analysis platforms optimized for performance.

Execution tracing has an observer effect: the act of tracing perturbs program behaviour via its overhead, which can in turn affect the accuracy of subsequent dynamic analysis. We investigate this observer effect in the context of concurrent behaviour within JVM-based programs. Concurrent behaviour is especially fragile as task-scheduling ordering can change, which could even lead to deadlock via thread starvation under certain conditions. We analyse three dimensions of overhead, compute volume, memory volume, and uniformity, using a configurable-overhead tracer and a concurrency-performance analyser. We argue that uniformity is a key, and underappreciated, dimension of overhead that can have qualitative effects on program behaviour. Experimental results show that overhead significantly affects real-world concurrent behaviour and subsequent analysis, at times unintuitively.

Developers are starting to write large and complex applications in TypeScript, a typed dialect of JavaScript. TypeScript applications integrate JavaScript libraries via typed descriptions of their APIs called declaration files. DefinitelyTyped is the standard public repository for these files. The repository is populated and maintained manually by volunteers, which is error-prone and time consuming. Discrepancies between a declaration file and the JavaScript implementation lead to incorrect feedback from the TypeScript IDE and, thus, to incorrect uses of the underlying JavaScript library.

This work presents dts-generate, a tool that generates TypeScript declaration files for JavaScript libraries uploaded to the NPM registry. It extracts code examples from the documentation written by the developer, executes the library driven by the examples, gathers run-time information, and generates a declaration file based on this information. To evaluate the tool, 249 declaration files were generated directly from an NPM module and 111 of these were compared with the corresponding declaration file provided on DefinitelyTyped. All these files either exhibited no differences at all or differences that can be resolved by extending the developer-provided examples.

This paper investigates the use of staged meta-programming techniques for the transparent acceleration of embedded domain-specific languages on the Java platform. LLJava-live, the staged API of the low-level JVM language LLJava, can be used to complement an interpreted EDSL with orthogonal and extensible compilation facilities. Compiled JVM bytecode becomes available immediately as an extension of the running host program. The approach is illustrated with a didactic structured imperative programming language, Whilst.

Code duplication is a major opportunity to enable optimizations in subsequent compiler phases. However, duplicating code prematurely or too liberally can result in tremendous code size increases. Thus, modern compilers use trade-offs between estimated costs in terms of code size increase and benefits in terms of performance increase. In the context of this ongoing research project, we propose the use of machine learning to provide trade-off functions with accurate predictions for code size impact. To evaluate our approach, we implemented a neural network predictor in the GraalVM compiler and compared its performance against a human-crafted, highly tuned heuristic. First results show promising performance improvements, leading to code size reductions of more than 10% for several benchmarks. Additionally, we present an assistance mode for finding flaws in the human-crafted heuristic, leading to improvements for the duplication optimization itself.