The growing gap between the advanced capabilities of static compilers as reflected in benchmarking results and the actual performance that users experience in real-life scenarios makes client-side dynamic optimization technologies imperative to the domain of static languages. Dynamic optimization of software distributed in the form of a platform-agnostic Intermediate-Representation (IR) has been very successful in the domain of managed languages, greatly improving upon interpreted code, especially when online profiling is used. However, can such feedback-directed IR-based dynamic code generation be viable in the domain of statically compiled, rather than interpreted, languages? We show that fat binaries, which combine the IR together with the statically compiled executable, can provide a practical solution for software vendors, allowing their software to be dynamically optimized without the limitation of binary-level approaches, which lack the high-level IR of the program, and without the warm-up costs associated with the IR-only software distribution approach. We describe and evaluate the fat-binary-based runtime compilation approach using SPECint2006, demonstrating that the overheads it incurs are low enough to be successfully surmounted by dynamic optimization. Building on Java JIT technologies, our results already improve upon common real-world usage scenarios, including very small workloads.

Just-in-Time (JIT) compiler technology offers portability while facilitating target- and context-specific specialization. Single-Instruction-Multiple-Data (SIMD) hardware is ubiquitous and markedly diverse, but can be difficult for JIT compilers to efficiently target due to resource and budget constraints. We present our design for a synergistic auto-vectorizing compilation scheme. The scheme is composed of an aggressive, generic offline stage coupled with a lightweight, target-specific online stage. Our method leverages the optimized intermediate results provided by the first stage across disparate SIMD architectures from different vendors, having distinct characteristics ranging from different vector sizes, memory alignment and access constraints, to special computational idioms. We demonstrate the effectiveness of our design using a set of kernels that exercise innermost loop, outer loop, as well as straight-line code vectorization, all automatically extracted by the common offline compilation stage. This results in performance comparable to that provided by specialized monolithic offline compilers. Our framework is implemented using open-source tools and standards, thereby promoting interoperability and extendibility.

Diversity is a confirmed trend of computing systems, which present a complex and moving target to software developers. Virtual machines and just-in-time compilers have been proposed to mitigate the complexity of these systems. They do so by offering a single and stable abstract machine model thereby hiding architectural details from programmers.

The high increase in usage of XML in electronic data exchange introduces new challenges for efficient processing of XML data. Applications that heavily use XML need to be able to quickly extract the relevant parts of the XML data, often using the XPath language for addressing XML document parts. High speed execution of XPath requests and queries is therefore becoming a critical requirement in many application domains, including XML databases and event processing. This work explores the potential for accelerating XPath processing in these domains using specialized hardware. This in turn poses the challenges of integrating specialized hardware with general-purpose application code. We present the design decisions behind building an integration layer to bridge between applications and the hardware, and describe our implementation. We discuss the factors that affect the acceleration potential, and show that despite the transmission overheads associated with off-loading XPath processing to the specialized co-processor, significant speedups can be obtained, ranging from modest 11% improvements in the event-processing domain, to over 6x speedup factor in the healthcare domain.