• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.10 no.4
• /
• pp.645-651
• /
• 2006

This paper discusses computer system performance evaluation and analysis by employing a simulator which able to execute a symmetric multiprocessor in machine simulation environment. We also perform a multiprocessor system analysis using SPLASH-2, which is a suite of multi-program benchmarks for multiprocessors, to perform the behavior study of the symmetric multiprocessor OS kernel, IRIX5.3. To validate the scalability of symmetric multiprocessor system, we demonstrate structure and evaluation methods for symmetric multiprocessor as well as a functionality-based software simulator, SimOS. In this paper, we examine cache miss count and stall time on the symmetric multiprocessor between the local instruction and local data, using the multi-program benchmarks such as RADIX sorting algorithm and Cholesky factorization.

• Journal of Information Processing Systems
• /
• v.8 no.2
• /
• pp.331-346
• /
• 2012

Task-based programming is becoming the state-of-the-art method of choice for extracting the desired performance from multi-core chips. It expresses a program in terms of lightweight logical tasks rather than heavyweight threads. Intel Threading Building Blocks (TBB) is a task-based parallel programming paradigm for multi-core processors. The performance gain of this paradigm depends to a great extent on the efficiency of its parallel constructs. The parallel overheads incurred by parallel constructs determine the ability for creating large-scale parallel programs, especially in the case of fine-grain parallelism. This paper presents a study of TBB parallelization overheads. For this purpose, a TBB micro-benchmarks suite called TBBench has been developed. We use TBBench to evaluate the parallelization overheads of TBB on different multi-core machines and different compilers. We report in detail in this paper on the relative overheads and analyze the running results.

• Journal of the Korea Society of Computer and Information
• /
• v.22 no.11
• /
• pp.1-7
• /
• 2017

In this paper, we introduce three monitoring filtering techniques which reduce the overheads of dynamic data race detection. It is well known that detecting data races dynamically in multi-threaded programs is quite hard and troublesome task, because the dynamic detection techniques need to monitor all execution of a multi-threaded program and to analyse every conflicting memory and thread operations in the program. Thus, the main drawback of the dynamic analysis for detecting data races is the heavy additional time and space overheads for running the program. For the practicality, we also empirically compare the efficiency of three monitoring filtering techniques. The results using OpenMP benchmarks show that the filtering techniques are practical for dynamic data race detection, since they reduce the average runtime overhead to under 10% of that of the pure detection.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.14 no.1
• /
• pp.138-144
• /
• 2010

Tachyon primary system which introduces recently is a high performance supercomputer that composed with AMD Barcelona nodes. In this paper, we will verify the performance and parallel scalability of TachyonIn by using multi-zone NAS Parallel Benchmark(NPB) which is one of a program with hybrid parallel method. To test performance of hybrid parallel execution, B and C classes of BT-MZ in NPB version 3.3 were used. And the parallel scalability test has finished with Tachyon's 1024 processes. It is the first time in Korea to get a result of hybrid parallel computing calculation using more than 1024 processes. Hybrid parallel method in high performance computing system with multi-core technology like Tachyon describes that it can be very efficient and useful parallel performance benchmarks.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.47 no.6
• /
• pp.57-67
• /
• 2010

This paper studies thermal characteristics of a mix of CPU-intensive and memory-intensive application workloads on a multi-core processor. Especially, we focus on thermal variations during program execution because thermal variations are more critical than average temperatures and their ranges for thermal management. New metrics are proposed to quantify such thermal variations for a workload. We study the thermal variations using SPEC CPU2006 benchmarks with varying cooling conditions and frequencies on an Intel Core 2 Duo processor. The results show that applications have distinct thermal variations characteristics. Such variations are affected by cooling conditions,operating frequencies and multiprogramming workload. Also, there are distinct spatial thermal variations between cores. Our new metrics and their results from this study provide useful insight for future research on multi-core thermal management.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2022.06a
• /
• pp.205-208
• /
• 2022

Multi-exposure high dynamic range (HDR) image reconstruction, the task of reconstructing an HDR image from multiple low dynamic range (LDR) images in a dynamic scene, often produces ghosting artifacts caused by camera motion and moving objects and also cannot deal with washed-out regions due to over or under-exposures. While there has been many deep-learning-based methods with motion estimation to alleviate these problems, they still have limitations for severely moving scenes. They also require large parameter counts, especially in the case of state-of-the-art methods that employ attention modules. To address these issues, we propose a frequency domain approach based on the idea that the transform domain coefficients inherently involve the global information from whole image pixels to cope with large motions. Specifically we adopt Residual Fast Fourier Transform (RFFT) blocks, which allows for global interactions of pixels. Moreover, we also employ Depthwise Overparametrized convolution (DO-conv) blocks, a convolution in which each input channel is convolved with its own 2D kernel, for faster convergence and performance gains. We call this LFFNet (Lightweight Frequency Fusion Network), and experiments on the benchmarks show reduced ghosting artifacts and improved performance up to 0.6dB tonemapped PSNR compared to recent state-of-the-art methods. Our architecture also requires fewer parameters and converges faster in training.