scholarly journals A Multithreading Based Enhanced Process Scheduling Technique for Heterogeneous Distributed Environment

2021 ◽  
pp. 125-129
Author(s):  
Krishan Kumar ◽  
Renu
Keyword(s):  
Optimal Schedule ◽  
Fixed Number ◽  
Instruction Level Parallelism ◽  
Processing Unit ◽  
Scheduling Problem ◽  
Process Scheduling ◽  
Central Processing ◽  
Gang Scheduling ◽  
Level Parallelism ◽  
Multi Core Processor

Multithreading is ability of a central processing unit (CPU) or a single core within a multi-core processor to execute multiple processes or threads concurrently, appropriately supported by operating system. This approach differs from multiprocessing, as with multithreading processes & threads have to share resources of a single or multiple cores: computing units, CPU caches, & translation lookaside buffer (TLB). Multiprocessing systems include multiple complete processing units, multithreading aims to increase utilization of a single core by using thread-level as well as instruction-level parallelism. Objective of research is increase efficiency of scheduling dependent task using enhanced multithreading. gang scheduling of parallel implicit-deadline periodic task systems upon identical multiprocessor platforms is considered. In this scheduling problem, parallel tasks use several processors simultaneously. first algorithm is based on linear programming & is first one to be proved optimal for considered gang scheduling problem. Furthermore, it runs in polynomial time for a fixed number m of processors & an efficient implementation is fully detailed. Second algorithm is an approximation algorithm based on a fixed-priority rule that is competitive under resource augmentation analysis in order to compute an optimal schedule pattern. Precisely, its speedup factor is bounded by (2?1/m). Both algorithms are also evaluated through intensive numerical experiments. In our research we have enhanced capability of Gang Scheduling by integration of multi core processor & Cache & make simulation of performance in MATLAB.

scholarly journals Scheduling Two Identical Parallel Machines Subjected to Release Times, Delivery Times and Unavailability Constraints

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1025
Author(s):  
Adel M. Al-Shayea ◽  
Mustafa Saleh ◽  
Moath Alatefi ◽  
Mageed Ghaleb
Keyword(s):  
Parallel Machines ◽  
Release Time ◽  
Processing Unit ◽  
Scheduling Problem ◽  
Identical Parallel Machines ◽  
Central Processing ◽  
Cpu Time ◽  
Release Times ◽  
Delivery Times ◽  
Input Variables

This paper proposes a genetic algorithm (GA) for scheduling two identical parallel machines subjected to release times and delivery times, where the machines are periodically unavailable. To make the problem more practical, we assumed that the machines are undergoing periodic maintenance rather than making them always available. The objective is to minimize the makespan (Cmax). A lower bound (LB) of the makespan for the considered problem was proposed. The GA performance was evaluated in terms of the relative percentage deviation (RPD) (the relative distance to the LB) and central processing unit (CPU) time. Response surface methodology (RSM) was used to optimize the GA parameters, namely, population size, crossover probability, mutation probability, mutation ratio, and pressure selection, which simultaneously minimize the RPD and CPU time. The optimized settings of the GA parameters were used to further analyze the scheduling problem. Factorial design of the scheduling problem input variables, namely, processing times, release times, delivery times, availability and unavailability periods, and number of jobs, was used to evaluate their effects on the RPD and CPU time. The results showed that increasing the release time intervals, decreasing the availability periods, and increasing the number of jobs increase the RPD and CPU time and make the problem very difficult to reach the LB.

scholarly journals Scheduling Semiconductor Multihead Testers Using Metaheuristic Techniques Embedded with Lot-Specific and Configuration-Specific Information

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Yi-Feng Hung ◽  
Chi-Chung Wang ◽  
Gen-Han Wu
Keyword(s):  
Parallel Machine Scheduling ◽  
Test Plant ◽  
Specific Information ◽  
Processing Unit ◽  
Scheduling Problem ◽  
Central Processing ◽  
Parallel Machine Scheduling Problem ◽  
Metaheuristic Techniques ◽  
Traditional Approaches ◽  
Short Time

In the semiconductor back-end manufacturing, the device test central processing unit (CPU) is most costly and is typically the bottleneck machine at the test plant. A multihead tester contains a CPU and several test heads, each of which can be connected to a handler that processes one lot of the same device. The residence time of a lot is closely related to the product mix on test heads, which increases the complexity of this problem. It is critical for the test scheduling problem to reduce CPU's idle time and to increase tester utilization. In this paper, a multihead tester scheduling problem is formulated as an identical parallel machine scheduling problem with the objective of minimizing makespan. A heuristic grouping method is developed to obtain a good initial solution in a short time. Three metaheuristic techniques, using lot-specific and configuration-specific information, are proposed to receive a near-optimum and are compared to traditional approaches. Computational experiments show that a tabu search with lot-specific information outperforms all other competing approaches.

Perangkat keras komputer

2020 ◽  
Author(s):  
Roudati jannah
Keyword(s):  
Data Storage ◽  
Central Processing Unit ◽  
External Memory ◽  
Storage Device ◽  
Input Device ◽  
Processing Unit ◽  
Central Processing ◽  
Output Device

Perangkat keras komputer adalah bagian dari sistem komputer sebagai perangkat yang dapat diraba, dilihat secara fisik, dan bertindak untuk menjalankan instruksi dari perangkat lunak (software). Perangkat keras komputer juga disebut dengan hardware. Hardware berperan secara menyeluruh terhadap kinerja suatu sistem komputer. Prinsipnya sistem komputer selalu memiliki perangkat keras masukan (input/input device system) – perangkat keras premprosesan (processing/central processing unit) – perangkat keras luaran (output/output device system) – perangkat tambahan yang sifatnya opsional (peripheral) dan tempat penyimpanan data (storage device system/external memory).

CENTRAL PROCESSING UNIT (CPU)

2020 ◽  
Author(s):  
Ika Milia wahyunu Siregar
Keyword(s):  
Processing System ◽  
Central Processing Unit ◽  
Processing Unit ◽  
Central Processing

Perkembangan IT di dunia sangat pesat, mulai dari perkembangan sofware hingga hardware. Teknologi sekarang telah mendominasi sebagian besar di permukaan bumi ini. Karena semakin cepatnya perkembangan Teknologi, kita sebagai pengguna bisa ketinggalan informasi mengenai teknologi baru apabila kita tidak up to date dalam pengetahuan teknologi ini. Hal itu dapat membuat kita mudah tergiur dan tertipu dengan berbagai iklan teknologi tanpa memikirkan sisi negatifnya. Sebagai pengguna dari komputer, kita sebaiknya tahu seputar mengenai komponen-komponen komputer. Komputer adalah serangkaian mesin elektronik yang terdiri dari jutaan komponen yang dapat saling bekerja sama, serta membentuk sebuah sistem kerja yang rapi dan teliti. Sistem ini kemudian digunakan untuk dapat melaksanakan pekerjaan secara otomatis, berdasarkan instruksi (program) yang diberikan kepadanya. Istilah Hardware komputer atau perangkat keras komputer, merupakan benda yang secara fisik dapat dipegang, dipindahkan dan dilihat. Central Processing System/ Central Processing Unit (CPU) adalah salah satu jenis perangkat keras yang berfungsi sebagai tempat untuk pengolahan data atau juga dapat dikatakan sebagai otak dari segala aktivitas pengolahan seperti penghitungan, pengurutan, pencarian, penulisan, pembacaan dan sebagainya.

Perangkat lunak komputer

2020 ◽  
Author(s):  
Intan khadijah simatupang
Keyword(s):  
Data Storage ◽  
Central Processing Unit ◽  
External Memory ◽  
Storage Device ◽  
Input Device ◽  
Processing Unit ◽  
Central Processing ◽  
Output Device

Komputer adalah serangkaian mesin elektronik yang terdiri dari jutaan komponen yang dapat saling bekerja sama, serta membentuk sebuah sistem kerja yang rapi dan teliti. Sistem ini kemudian digunakan untuk dapat melaksanakan pekerjaan secara otomatis, berdasarkan instruksi (program) yang diberikan kepadanya. Istilah Hardware computer atau perangkat keras komputer, merupakan benda yang secara fisik dapat dipegang, dipindahkan dan dilihat. Software komputer atau perangkat lunak komputer merupakan kumpulan instruksi (program/prosedur) untuk dapat melaksanakan pekerjaan secara otomatis dengan cara mengolah atau memproses kumpulan instruksi (data) yang diberikan. Pada prinsipnya sistem komputer selalu memiliki perangkat keras masukan (input/input device system) – perangkat keras pemprosesan (processing/ central processing unit) – perangkat keras keluaran (output/output device system), perangkat tambahan yang sifatnya opsional (peripheral) dan tempat penyimpanan data (Storage device system/external memory).

PERANGKAT KERAS ( HARDWARE ) PADA KOMPUTER

2020 ◽  
Author(s):  
Siti Kumala Dewi
Keyword(s):  
Processing System ◽  
Central Processing Unit ◽  
Input Device ◽  
Processing Unit ◽  
Central Processing ◽  
Output Device ◽  
System P ◽  
System 2

Perangkat keras komputer adalah bagian dari sistem komputer sebagai perangkat yang dapat diraba, dilihat secara fisik, dan bertindak untuk menjalankan instruksi dari perangkat lunak (software). Perangkat keras komputer juga disebut dengan hardware. Hardware berperan secara menyeluruh terhadap kinerja suatu sistem komputer. Berdasarkan fungsinya, perangkat keras terbagi menjadi :1.Sistem Perangkat Keras Masukan (Input Device System )2.Sistem Pemrosesan ( Central Processing System/ Central Processing Unit(CPU)3.Sistem Perangkat Keras Keluaran ( Output Device System )4.Sistem Perangkat Keras Tambahan (Peripheral/Accessories Device System)

scholarly journals Microarchitectural Characterization on a Mobile Workload

2021 ◽  
Vol 11 (3) ◽  
pp. 1225
Author(s):  
Woohyong Lee ◽  
Jiyoung Lee ◽  
Bo Kyung Park ◽  
R. Young Chul Kim
Keyword(s):  
Performance Monitoring ◽  
Performance Metrics ◽  
Performance Comparison ◽  
Instruction Level Parallelism ◽  
Data Set ◽  
Performance Events ◽  
Hardware Performance Counters ◽  
On Chip ◽  
The Comparative Study ◽  
Level Parallelism

Geekbench is one of the most referenced cross-platform benchmarks in the mobile world. Most of its workloads are synthetic but some of them aim to simulate real-world behavior. In the mobile world, its microarchitectural behavior has been reported rarely since the hardware profiling features are limited to the public. As a popular mobile performance workload, it is hard to find Geekbench’s microarchitecture characteristics in mobile devices. In this paper, a thorough experimental study of Geekbench performance characterization is reported with detailed performance metrics. This study also identifies mobile system on chip (SoC) microarchitecture impacts, such as the cache subsystem, instruction-level parallelism, and branch performance. After the study, we could understand the bottleneck of workloads, especially in the cache sub-system. This means that the change of data set size directly impacts performance score significantly in some systems and will ruin the fairness of the CPU benchmark. In the experiment, Samsung’s Exynos9820-based platform was used as the tested device with Android Native Development Kit (NDK) built binaries. The Exynos9820 is a superscalar processor capable of dual issuing some instructions. To help performance analysis, we enable the capability to collect performance events with performance monitoring unit (PMU) registers. The PMU is a set of hardware performance counters which are built into microprocessors to store the counts of hardware-related activities. Throughout the experiment, functional and microarchitectural performance profiles were fully studied. This paper describes the details of the mobile performance studies above. In our experiment, the ARM DS5 tool was used for collecting runtime PMU profiles including OS-level performance data. After the comparative study is completed, users will understand more about the mobile architecture behavior, and this will help to evaluate which benchmark is preferable for fair performance comparison.

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