arXiv subjects

Wasuwee Sodsong

Publications and source records attributed to Wasuwee Sodsong.

3 recordsLinked to original sources

arXiv2023

Julia Cloud Matrix Machine: Dynamic Matrix Language Acceleration on Multicore Clusters in the Cloud

In emerging scientific computing environments, matrix computations of increasing size and complexity are increasingly becoming prevalent. However, contemporary matrix language implementations are insufficient in their support for efficient utilization of cloud computing resources, particularly on the user side. We thus developed an extension of the Julia high-performance computation language such that matrix computations are automatically parallelized in the cloud, where users are separated from directly interacting with complex explicitly-parallel computations. We implement lazy evaluation semantics combined with directed graphs to optimize matrix operations on the fly while dynamic simulation finds the optimal tile size and schedule for a given cluster of cloud nodes. A time model prediction of the cluster's performance capacity is constructed to enable simulations. Automatic configuration of communication and worker processes on the cloud networks allow for the framework to automatically scale up for clusters of heterogeneous nodes. Our framework's experimental evaluation comprises eleven benchmarks on an fourteen node (564 CPUs) cluster in the AWS public cloud, revealing speedups of up to a factor of 5.1, with an average 74.39% of the upper bound for speedups.

cs.DC↗

arXiv2017

SPARK: Static Program Analysis Reasoning and Retrieving Knowledge

Program analysis is a technique to reason about programs without executing them, and it has various applications in compilers, integrated development environments, and security. In this work, we present a machine learning pipeline that induces a security analyzer for programs by example. The security analyzer determines whether a program is either secure or insecure based on symbolic rules that were deduced by our machine learning pipeline. The machine pipeline is two-staged consisting of a Recurrent Neural Networks (RNN) and an Extractor that converts an RNN to symbolic rules. To evaluate the quality of the learned symbolic rules, we propose a sampling-based similarity measurement between two infinite regular languages. We conduct a case study using real-world data. In this work, we discuss the limitations of existing techniques and possible improvements in the future. The results show that with sufficient training data and a fair distribution of program paths it is feasible to deducing symbolic security rules for the OpenJDK library with millions lines of code.

cs.PL↗

arXiv2014

Dynamic Partitioning-based JPEG Decompression on Heterogeneous Multicore Architectures

With the emergence of social networks and improvements in computational photography, billions of JPEG images are shared and viewed on a daily basis. Desktops, tablets and smartphones constitute the vast majority of hardware platforms used for displaying JPEG images. Despite the fact that these platforms are heterogeneous multicores, no approach exists yet that is capable of joining forces of a system's CPU and GPU for JPEG decoding. In this paper we introduce a novel JPEG decoding scheme for heterogeneous architectures consisting of a CPU and an OpenCL-programmable GPU. We employ an offline profiling step to determine the performance of a system's CPU and GPU with respect to JPEG decoding. For a given JPEG image, our performance model uses (1) the CPU and GPU performance characteristics, (2) the image entropy and (3) the width and height of the image to balance the JPEG decoding workload on the underlying hardware. Our run-time partitioning and scheduling scheme exploits task, data and pipeline parallelism by scheduling the non-parallelizable entropy decoding task on the CPU, whereas inverse cosine transformations (IDCTs), color conversions and upsampling are conducted on both the CPU and the GPU. Our kernels have been optimized for GPU memory hierarchies. We have implemented the proposed method in the context of the libjpeg-turbo library, which is an industrial-strength JPEG encoding and decoding engine. Libjpeg-turbo's hand-optimized SIMD routines for ARM and x86 constitute a competitive yardstick for the comparison to the proposed approach. Retro-fitting our method with libjpeg-turbo provided insights on the software-engineering aspects of re-engineering legacy code for heterogeneous multicores.

cs.DC↗