Data Interface + Algorithms = Efficient Programs: Separating Logic from Representation to Improve Performance

Finding the right algorithm–data structure combination is easy, but finding the right data structure for a set of algorithms is much less trivial. Moreover, using the same data representation throughout the whole program might be sub-optimal. Depending on several factors, often only known at runtime, some programs benefit from changing the data representation during execution. In this position paper we introduce the idea of Just-In-Time data structures, a combination of a data interface and a set of concrete data representations with different performance characteristics. These Just-In- Time data structures can dynamically swap their internal data representation when the cost of swapping is payed back many times in the remainder of the computation. To make Just-In-Time data structures work, research is needed at three fronts: 1. We need to better understand the synergy between different data representations and algorithms; 2. We need a structured approach to handle the transitions between data representations; 3. We need descriptive programming constructs to express which representation fits a program fragment best. Combined, this research will result in a structured programming approach where separating data interface from data representation, not only improves understandability and maintainability, but also improves performance through automated transitions of data representation

Fork/Join Parallelism in the Wild: Documenting Patterns and Anti-Patterns in Java Programs using the Fork/Join Framework

Now that multicore processors are commonplace, developing parallel software has escaped the confines of high-performance computing and enters the mainstream. The Fork/Join framework, for instance, is part of the standard Java platform since version 7. Fork/Join is a high-level parallel programming model advocated to make parallelizing recursive divide-and-conquer algorithms particularly easy. While, in theory, Fork/Join is a simple and effective technique to expose parallelism in applications, it has not been investigated before whether and how the technique is applied in practice. We therefore performed an empirical study on a corpus of 120 open source Java projects that use the framework for roughly 362 different tasks. On the one hand, we confirm the frequent use of four best-practice patterns (Sequential Cutoff, Linked Subtasks, Leaf Tasks, and avoiding unnecessary forking) in actual projects. On the other hand, we also discovered three recurring anti-patterns that potentially limit parallel performance: sub-optimal use of Java collections when splitting tasks into subtasks as well as when merging the results of subtasks, and finally the inappropriate sharing of resources between tasks. We document these anti-patterns and study their impact on performance

Which Problems Does a Multi-Language Virtual Machine Need to Solve in the Multicore/Manycore Era?

While parallel programming for very regular problems has been used in the scientific community by non-computer-scientists successfully for a few decades now, concurrent programming and solving irregular problems remains hard. Furthermore, we shift from few expert system programmers mastering concurrency for a constrained set of problems to mainstream application developers being required to master concurrency for a wide variety of problems. Consequently, high-level language virtual machine (VM) research faces interesting questions. What are processor design changes that have an impact on the abstractions provided by VMs to provide platform independence? How can application programmers' diverse needs be facilitated to solve concurrent programming problems? We argue that VMs will need to be ready for a wide range of different concurrency models that allow solving concurrency problems with appropriate abstractions. Furthermore, they need to abstract from heterogeneous processor architectures, varying performance characteristics, need to account for memory access cost and inter-core communication mechanisms but should only expose the minimal useful set of notions like locality, explicit communication, and adaptable scheduling to maintain their abstracting nature. Eventually, language designers need to be enabled to guarantee properties like encapsulation, scheduling guarantees, and immutability also when an interaction between different problem-specific concurrency abstractions is required

Just-in-Time Data Structures

Today, software engineering practices focus on finding the single "right" data representation (i.e., data structure) for a program. The right data representation, however, might not exist: relying on a single representation of the data for the lifetime of the program can be suboptimal in terms of performance. We explore the idea of developing data structures for which changing the data representation is an intrinsic property. To this end we introduce Just-in-Time Data Structures, which enable representation changes at runtime, based on declarative input from a performance expert programmer. Just-in-Time Data Structures are an attempt to shift the focus from finding the "right" data structure to finding the right sequence of data representations. We present JitDS-Java, an extension to the Java language, to develop Just-in-Time Data Structures. Further, we show two example programs that benefit from changing the representation at runtime

Partitioned Global Address Space Languages

The Partitioned Global Address Space (PGAS) model is a parallel programming model that aims to improve programmer productivity while at the same time aiming for high performance. The main premise of PGAS is that a globally shared address space improves productivity, but that a distinction between local and remote data accesses is required to allow performance optimizations and to support scalability on large-scale parallel architectures. To this end, PGAS preserves the global address space while embracing awareness of non-uniform communication costs. Today, about a dozen languages exist that adhere to the PGAS model. This survey proposes a definition and a taxonomy along four axes: how parallelism is introduced, how the address space is partitioned, how data is distributed among the partitions and finally how data is accessed across partitions. Our taxonomy reveals that today's PGAS languages focus on distributing regular data and distinguish only between local and remote data access cost, whereas the distribution of irregular data and the adoption of richer data access cost models remain open challenges