The performance of contemporary object oriented languages depends on optimizations such as devirtualization, inlining, and specialization, and these in turn depend on precise call graph analysis. Existing call graph analyses do not take advantage of the information provided by the rich type systems of contemporary languages, in particular generic type arguments. Many existing approaches analyze Java bytecode, in which generic types have been erased. This paper shows that this discarded information is actually very useful as the context in a context-sensitive analysis, where it significantly improves precision and keeps the running time small. Specifically, we propose and evaluate call graph construction algorithms in which the contexts of a method are (i) the type arguments passed to its type parameters, and (ii) the static types of the arguments passed to its term parameters. The use of static types from the caller as context is effective because it allows more precise dispatch of call sites inside the callee.

State-of-the-art immutable collections have wildly differing performance characteristics across their operations, often forcing programmers to choose different collection implementations for each task. Thus, changes to the program can invalidate the choice of collections, making code evolution costly. It would be desirable to have a collection that performs well for a broad range of operations. To this end, we present the RRB-Vector, an immutable sequence collection that offers good performance across a large number of sequential and parallel operations. The underlying innovations are: (1) the Relaxed-Radix-Balanced (RRB) tree structure, which allows efficient structural reorganization, and (2) an optimization that exploits spatio-temporal locality on the RRB data structure in order to offset the cost of traversing the tree. In our benchmarks, the RRB-Vector speedup for parallel operations is lower bounded by 7x when executing on 4 CPUs of 8 cores each. The performance for discrete operations, such as appending on either end, or updating and removing elements, is consistently good and compares favorably to the most important immutable sequence collections in the literature and in use today. The memory footprint of RRB-Vector is on par with arrays and an order of magnitude less than competing collections.

To maximize run-time performance, programmers often specialize their code by hand, replacing library collections and containers by custom objects in which data is restructured for efficient access. However, changing the data representation is a tedious and error-prone process that makes it hard to test, maintain and evolve the source code. We present an automated and composable mechanism that allows programmers to safely change the data representation in delimited scopes containing anything from expressions to entire class definitions. To achieve this, programmers define a transformation and our mechanism automatically and transparently applies it during compilation, eliminating the need to manually change the source code. Our technique leverages the type system in order to offer correctness guarantees on the transformation and its interaction with object-oriented language features, such as dynamic dispatch, inheritance and generics. We have embedded this technique in a Scala compiler plugin and used it in four very different transformations, ranging from improving the data layout and encoding, to retrofitting specialization and value class status, and all the way to collection deforestation. On our benchmarks, the technique obtained speedups between 1.8x and 24.5x.

Values need to be represented differently when interacting with certain language features. For example, an integer has to take an object-based representation when interacting with erased generics, although, for performance reasons, the stack-based value representation is better. To abstract over these implementation details, some programming languages choose to expose a unified high-level concept (the integer) and let the compiler choose its exact representation and insert coercions where necessary.

Parametric polymorphism enables code reuse and type safety. Underneath the uniform interface exposed to programmers, however, its low level implementation has to cope with inherently non-uniform data: value types of different sizes and semantics (bytes, integers, floating point numbers) and reference types (pointers to heap objects). On the Java Virtual Machine, parametric polymorphism is currently translated to bytecode using two competing approaches: homogeneous and heterogeneous. Homogeneous translation requires boxing, and thus introduces indirect access delays. Heterogeneous translation duplicates and adapts code for each value type individually, producing more bytecode. Therefore bytecode speed and size are at odds with each other. This paper proposes a novel translation that significantly reduces the bytecode size without affecting the execution speed. The key insight is that larger value types (such as integers) can hold smaller ones (such as bytes) thus reducing the duplication necessary in heterogeneous translations. In our implementation, on the Scala compiler, we encode all primitive value types in long integers. The resulting bytecode approaches the performance of monomorphic code, matches the performance of the heterogeneous translation and obtains speedups of up to 22x over the homogeneous translation, all with modest increases in size.

Domain-specific languages (DSLs) can bridge the gap between high-level programming and efficient execution. However, implementing compiler tool-chains for performance oriented DSLs requires significant effort. Recent research has produced methodologies and frameworks that promise to reduce this development effort by enabling quick transition from library-only, purely embedded DSLs to optimizing compilation. In this case study we report on our experience implementing a compiler for StagedSAC. StagedSAC is a DSL for arithmetic processing with multidimensional arrays modeled after the stand-alone language SAC (Single Assignment C). The main language feature of both SAC and StagedSAC is a loop construction that enables high-level and concise implementations of array algorithms. At the same time, the functional semantics of the two languages allow for advanced compiler optimizations and parallel code generation.

This paper introduces Cloud9, a platform for automated testing of real-world software. Our main contribution is the scalable parallelization of symbolic execution on clusters of commodity hardware, to help cope with path explosion. Cloud9 provides a systematic interface for writing "symbolic tests" that concisely specify entire families of inputs and behaviors to be tested, thus improving testing productivity. Cloud9 can handle not only single-threaded programs but also multi-threaded and distributed systems. It includes a new symbolic environment model that is the first to support all major aspects of the POSIX interface, such as processes, threads, synchronization, networking, IPC, and file I/O. We show that Cloud9 can automatically test real systems, like memcached, Apache httpd, lighttpd, the Python interpreter, rsync, and curl. We show how Cloud9 can use existing test suites to generate new test cases that capture untested corner cases (e.g., network stream fragmentation). Cloud9 can also diagnose incomplete bug fixes by analyzing the difference between buggy paths before and after a patch.