Optimizing Abstract Abstract Machines

The technique of abstracting abstract machines (AAM) provides a systematic approach for deriving computable approximations of evaluators that are easily proved sound. This article contributes a complementary step-by-step process for subsequently going from a naive analyzer derived under the AAM approach, to an efficient and correct implementation. The end result of the process is a two to three order-of-magnitude improvement over the systematically derived analyzer, making it competitive with hand-optimized implementations that compute fundamentally less precise results.Comment: Proceedings of the International Conference on Functional Programming 2013 (ICFP 2013). Boston, Massachusetts. September, 201

Towards a design of HMO, an integrated hardware microcode optimizer

This paper discusses an algorithm for optimizing the density and parallelism of microcoded routines in micro-programmable machines. Besides presenting the algorithm itself, this research also analyzes the algorithm\u27s uses, design integration problems, architectural requirements, and adaptability to conventional machine characteristics. Even though the paper proposes a hardware implementation of the algorithm, the algorithm is viewed as an integral part of the entire microcode generation and usage process, from initial high-level input into a software microcode compiler down to machine-level execution of the resultant microcode on the host machine. It is believed that, by removing much of the traditionally time-consuming and machine-dependent microcode optimization from the software portion of this process, the algorithm can improve the overall process --Abstract, page ii

Hybrid ASP-based multi-objective scheduling of semiconductor manufacturing processes (Extended version)

Modern semiconductor manufacturing involves intricate production processes consisting of hundreds of operations, which can take several months from lot release to completion. The high-tech machines used in these processes are diverse, operate on individual wafers, lots, or batches in multiple stages, and necessitate product-specific setups and specialized maintenance procedures. This situation is different from traditional job-shop scheduling scenarios, which have less complex production processes and machines, and mainly focus on solving highly combinatorial but abstract scheduling problems. In this work, we address the scheduling of realistic semiconductor manufacturing processes by modeling their specific requirements using hybrid Answer Set Programming with difference logic, incorporating flexible machine processing, setup, batching and maintenance operations. Unlike existing methods that schedule semiconductor manufacturing processes locally with greedy heuristics or by independently optimizing specific machine group allocations, we examine the potentials of large-scale scheduling subject to multiple optimization objectives.Comment: 17 pages, 1 figure, 4 listings, 1 table; a short version of this paper is presented at the 18th European Conference on Logics in Artificial Intelligence (JELIA 2023

SYNERGIA: A MODERN TOOL FOR ACCELERATOR PHYSICS SIMULATION

Abstract High precision modeling of space-charge effects, together with accurate treatment of single-particle dynamics, is essential for designing future accelerators as well as optimizing the performance of existing machines. Synergia is a high-fidelity parallel beam dynamics simulation package with fully three dimensional space-charge capabilities and a higher order optics implementation. We describe the computational techniques, the advanced human interface, and the parallel performance obtained using large numbers of macroparticles

Elastic Allocation of Docker Containers in Cloud Environments

Abstract Docker containers wrap up a piece of software together with everything it needs for the execution and enable to easily run it on any machine. For their execution in the Cloud, we need to identify an elastic set of virtual machines that can accommodate those containers, while considering the diversity of their requirements. In this paper, we briefly describe our formulation of the Elastic provisioning of Virtual machines for Container Deployment (EVCD), which takes explicitly into account the heterogeneity of container requirements and virtual machine resources. Afterwards, we evaluate the EVCD formulation with the aim of demonstrating its flexibility in optimizing multiple QoS metrics

Optimizing the SICStus Prolog virtual machine instruction set

The Swedish Institute of Computer Science (SICS) is the vendor of SICStus Prolog. To decrease execution time and reduce space requirements, variants of SICStus Prolog's virtual instruction set were investigated. Semi-automatic ways of finding candidate sets of instructions to combine or specialize were developed and used. Several virtual machines were implemented and the relationship between improvements by combinations and by specializations were investigated. The benefits of specializations and combinations of instructions to the performance of the emulator is on the average of the order of 10%. The code size reduction is 15%