Control theory for principled heap sizing

We propose a new, principled approach to adaptive heap sizing based on control theory. We review current state-of-the-art heap sizing mechanisms, as deployed in Jikes RVM and HotSpot. We then formulate heap sizing as a control problem, apply and tune a standard controller algorithm, and evaluate its performance on a set of well-known benchmarks. We find our controller adapts the heap size more responsively than existing mechanisms. This responsiveness allows tighter virtual machine memory footprints while preserving target application throughput, which is ideal for both embedded and utility computing domains. In short, we argue that formal, systematic approaches to memory management should be replacing ad-hoc heuristics as the discipline matures. Control-theoretic heap sizing is one such systematic approach

An Efficient Monte Carlo-based Probabilistic Time-Dependent Routing Calculation Targeting a Server-Side Car Navigation System

Incorporating speed probability distribution to the computation of the route planning in car navigation systems guarantees more accurate and precise responses. In this paper, we propose a novel approach for dynamically selecting the number of samples used for the Monte Carlo simulation to solve the Probabilistic Time-Dependent Routing (PTDR) problem, thus improving the computation efficiency. The proposed method is used to determine in a proactive manner the number of simulations to be done to extract the travel-time estimation for each specific request while respecting an error threshold as output quality level. The methodology requires a reduced effort on the application development side. We adopted an aspect-oriented programming language (LARA) together with a flexible dynamic autotuning library (mARGOt) respectively to instrument the code and to take tuning decisions on the number of samples improving the execution efficiency. Experimental results demonstrate that the proposed adaptive approach saves a large fraction of simulations (between 36% and 81%) with respect to a static approach while considering different traffic situations, paths and error requirements. Given the negligible runtime overhead of the proposed approach, it results in an execution-time speedup between 1.5x and 5.1x. This speedup is reflected at infrastructure-level in terms of a reduction of around 36% of the computing resources needed to support the whole navigation pipeline

GPUs as Storage System Accelerators

Massively multicore processors, such as Graphics Processing Units (GPUs), provide, at a comparable price, a one order of magnitude higher peak performance than traditional CPUs. This drop in the cost of computation, as any order-of-magnitude drop in the cost per unit of performance for a class of system components, triggers the opportunity to redesign systems and to explore new ways to engineer them to recalibrate the cost-to-performance relation. This project explores the feasibility of harnessing GPUs' computational power to improve the performance, reliability, or security of distributed storage systems. In this context, we present the design of a storage system prototype that uses GPU offloading to accelerate a number of computationally intensive primitives based on hashing, and introduce techniques to efficiently leverage the processing power of GPUs. We evaluate the performance of this prototype under two configurations: as a content addressable storage system that facilitates online similarity detection between successive versions of the same file and as a traditional system that uses hashing to preserve data integrity. Further, we evaluate the impact of offloading to the GPU on competing applications' performance. Our results show that this technique can bring tangible performance gains without negatively impacting the performance of concurrently running applications.Comment: IEEE Transactions on Parallel and Distributed Systems, 201

Real world evaluation of aspect-oriented software development : a thesis submitted in partial fulfilment of the requirements for the degree of Master of Science in Computer Science at Massey University, Palmerston North, New Zealand

Software development has improved over the past decade with the rise in the popularity of the Object-Oriented (OO) development approach. However, software projects continue to grow in complexity and continue to have alarmingly low rates of success. Aspect-Oriented Programming (AOP) is touted to be one solution to this software development problem. It shows promise of reducing programming complexity, making software more flexible and more amenable to change. The central concept introduced by AOP is the aspect. An aspect is used to modularise crosscutting concerns in a similar fashion to the way classes modularise business concerns. A crosscutting concern cannot be modularised in approaches such as OO because the code to realise the concern must be spread throughout the module (e.g. a tracing concent is implemented by adding code to every method in a system). AOP also introduces join points, pointcuts, and advice which are used with aspects to capture crosscutting concerns so they can be localised in a modular unit. OO took approximately 20 years to become a mainstream development approach. AOP was only invented in 1997. This project considers whether AOP is ready for commercial adoption. This requires analysis of the AOP implementations available, tool support, design processes, testing tools, standards, and support infrastructure. Only when AOP is evaluated across all these criteria can it be established whether it is ready to be used in commercial projects. Moreover, if companies are to invest time and money into adopting AOP, they must be aware of the benefits and risks associated with its adoption. This project attempts to quantify the potential benefits in adopting AOP, as well as identifying areas of risk. SolNet Solutions Ltd, an Information Technology (IT) company in Wellington, New Zealand, is used in this study as a target environment for integration of aspects into a commercial development process. SolNet is in the business of delivering large scale enterprise Java applications. To assist in this process they have developed a Common Services Architecture (CSA) containing components that can be reused to reduce risk and cost to clients. However, the CSA is complicated and SolNet have identified aspects as a potential solution to decrease the complexity. Aspects were found to bring substantial improvement to the Service Layer of SolNet. applications, including substantial reductions in complexity and size. This reduces the cost and time of development, as well as the risk associated with the projects. Moreover, the CSA was used in a more consistent fashion making the system easier to understand and maintain, and several crosscutting concerns were modularised as part of a reusable aspect library which could eventually form part of their CSA. It was found that AOP is approaching commercial readiness. However, more work is needed on defining standards for aspect languages and modelling of design elements. The current solutions in this area are commercially viable, but would greatly benefit from a standardised approach. Aspect systems can be difficult to test and the effect of the weaving process on Java serialisation requires further investigation

Adaptive runtime-assisted block prefetching on chip-multiprocessors

Memory stalls are a significant source of performance degradation in modern processors. Data prefetching is a widely adopted and well studied technique used to alleviate this problem. Prefetching can be performed by the hardware, or be initiated and controlled by software. Among software controlled prefetching we find a wide variety of schemes, including runtime-directed prefetching and more specifically runtime-directed block prefetching. This paper proposes a hybrid prefetching mechanism that integrates a software driven block prefetcher with existing hardware prefetching techniques. Our runtime-assisted software prefetcher brings large blocks of data on-chip with the support of a low cost hardware engine, and synergizes with existing hardware prefetchers that manage locality at a finer granularity. The runtime system that drives the prefetch engine dynamically selects which cache to prefetch to. Our evaluation on a set of scientific benchmarks obtains a maximum speed up of 32 and 10 % on average compared to a baseline with hardware prefetching only. As a result, we also achieve a reduction of up to 18 and 3 % on average in energy-to-solution.Peer ReviewedPostprint (author's final draft