This document reviews parallel computing and compares different parallel programming models. It discusses CPU and GPU architectures, highlighting that GPUs are designed for massive parallelism while CPUs balance computing power and flexibility. The document evaluates programming models based on supported system architectures, programming interfaces, workload partitioning, task assignment, synchronization methods, and communication models.
Is Multicore Hardware For General-Purpose Parallel Processing Broken? : NotesSubhajit Sahu
Highlighted notes of article while studying Concurrent Data Structures, CSE:
Is Multicore Hardware For General-Purpose Parallel Processing Broken?
By Uzi Vishkin
Communications of the ACM, April 2014, Vol. 57 No. 4, Pages 35-39
10.1145/2580945
Concurrent Matrix Multiplication on Multi-core ProcessorsCSCJournals
With the advent of multi-cores every processor has built-in parallel computational power and that can only be fully utilized only if the program in execution is written accordingly. This study is a part of an on-going research for designing of a new parallel programming model for multi-core architectures. In this paper we have presented a simple, highly efficient and scalable implementation of a common matrix multiplication algorithm using a newly developed parallel programming model SPC3 PM for general purpose multi-core processors. From our study it is found that matrix multiplication done concurrently on multi-cores using SPC3 PM requires much less execution time than that required using the present standard parallel programming environments like OpenMP. Our approach also shows scalability, better and uniform speedup and better utilization of available cores than that the algorithm written using standard OpenMP or similar parallel programming tools. We have tested our approach for up to 24 cores with different matrices size varying from 100 x 100 to 10000 x 10000 elements. And for all these tests our proposed approach has shown much improved performance and scalability
Hardback solution to accelerate multimedia computation through mgp in cmpeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Highlighted notes on Hybrid Multicore Computing
While doing research work under Prof. Dip Banerjee, Prof. Kishore Kothapalli.
In this comprehensive report, Prof. Dip Banerjee describes about the benefit of utilizing both multicore systems, CPUs with vector instructions, and manycore systems, GPUs with large no. of low speed ALUs. Such hybrid systems are beneficial to several algorithms as an accelerator cant optimize for all parts of an algorithms (some computations are very regular, while some very irregular).
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online
ABOUT THE SUITABILITY OF CLOUDS IN HIGH-PERFORMANCE COMPUTINGcsandit
Cloud computing has become the ubiquitous computing and storage paradigm. It is also attractive for scientists, because they do not have to care any more for their own IT
infrastructure, but can outsource it to a Cloud Service Provider of their choice. However, for the case of High-Performance Computing (HPC) in a cloud, as it is needed in simulations or for Big Data analysis, things are getting more intricate, because HPC codes must stay highly
efficient, even when executed by many virtual cores (vCPUs). Older clouds or new standard clouds can fulfil this only under special precautions, which are given in this article. The results
can be extrapolated to other cloud OSes than OpenStack and to other codes than OpenFOAM,which were used as examples
ABOUT THE SUITABILITY OF CLOUDS IN HIGH-PERFORMANCE COMPUTINGcscpconf
Cloud computing has become the ubiquitous computing and storage paradigm. It is also attractive for scientists, because they do not have to care any more for their own IT infrastructure, but can outsource it to a Cloud Service Provider of their choice. However, for the case of High-Performance Computing (HPC) in a cloud, as it is needed in simulations or for Big Data analysis, things are getting more intricate, because HPC codes must stay highly efficient, even when executed by many virtual cores (vCPUs). Older clouds or new standard clouds can fulfil this only under special precautions, which are given in this article. The results can be extrapolated to other cloud OSes than OpenStack and to other codes than OpenFOAM, which were used as examples.
Is Multicore Hardware For General-Purpose Parallel Processing Broken? : NotesSubhajit Sahu
Highlighted notes of article while studying Concurrent Data Structures, CSE:
Is Multicore Hardware For General-Purpose Parallel Processing Broken?
By Uzi Vishkin
Communications of the ACM, April 2014, Vol. 57 No. 4, Pages 35-39
10.1145/2580945
Concurrent Matrix Multiplication on Multi-core ProcessorsCSCJournals
With the advent of multi-cores every processor has built-in parallel computational power and that can only be fully utilized only if the program in execution is written accordingly. This study is a part of an on-going research for designing of a new parallel programming model for multi-core architectures. In this paper we have presented a simple, highly efficient and scalable implementation of a common matrix multiplication algorithm using a newly developed parallel programming model SPC3 PM for general purpose multi-core processors. From our study it is found that matrix multiplication done concurrently on multi-cores using SPC3 PM requires much less execution time than that required using the present standard parallel programming environments like OpenMP. Our approach also shows scalability, better and uniform speedup and better utilization of available cores than that the algorithm written using standard OpenMP or similar parallel programming tools. We have tested our approach for up to 24 cores with different matrices size varying from 100 x 100 to 10000 x 10000 elements. And for all these tests our proposed approach has shown much improved performance and scalability
Hardback solution to accelerate multimedia computation through mgp in cmpeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Highlighted notes on Hybrid Multicore Computing
While doing research work under Prof. Dip Banerjee, Prof. Kishore Kothapalli.
In this comprehensive report, Prof. Dip Banerjee describes about the benefit of utilizing both multicore systems, CPUs with vector instructions, and manycore systems, GPUs with large no. of low speed ALUs. Such hybrid systems are beneficial to several algorithms as an accelerator cant optimize for all parts of an algorithms (some computations are very regular, while some very irregular).
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online
ABOUT THE SUITABILITY OF CLOUDS IN HIGH-PERFORMANCE COMPUTINGcsandit
Cloud computing has become the ubiquitous computing and storage paradigm. It is also attractive for scientists, because they do not have to care any more for their own IT
infrastructure, but can outsource it to a Cloud Service Provider of their choice. However, for the case of High-Performance Computing (HPC) in a cloud, as it is needed in simulations or for Big Data analysis, things are getting more intricate, because HPC codes must stay highly
efficient, even when executed by many virtual cores (vCPUs). Older clouds or new standard clouds can fulfil this only under special precautions, which are given in this article. The results
can be extrapolated to other cloud OSes than OpenStack and to other codes than OpenFOAM,which were used as examples
ABOUT THE SUITABILITY OF CLOUDS IN HIGH-PERFORMANCE COMPUTINGcscpconf
Cloud computing has become the ubiquitous computing and storage paradigm. It is also attractive for scientists, because they do not have to care any more for their own IT infrastructure, but can outsource it to a Cloud Service Provider of their choice. However, for the case of High-Performance Computing (HPC) in a cloud, as it is needed in simulations or for Big Data analysis, things are getting more intricate, because HPC codes must stay highly efficient, even when executed by many virtual cores (vCPUs). Older clouds or new standard clouds can fulfil this only under special precautions, which are given in this article. The results can be extrapolated to other cloud OSes than OpenStack and to other codes than OpenFOAM, which were used as examples.
Cloud computing is an emerging technology. It process huge amount of data so scheduling mechanism
works as a vital role in the cloud computing. Thus my protocol is designed to minimize the switching time,
improve the resource utilization and also improve the server performance and throughput. This method or
protocol is based on scheduling the jobs in the cloud and to solve the drawbacks in the existing protocols.
Here we assign the priority to the job which gives better performance to the computer and try my best to
minimize the waiting time and switching time. Best effort has been made to manage the scheduling of jobs
for solving drawbacks of existing protocols and also improvise the efficiency and throughput of the server.
Towards high performance computing(hpc) through parallel programming paradigm...ijpla
Nowadays, we are to find out solutions to huge computing problems very rapidly. It brings the idea of parallel computing in which several machines or processors work cooperatively for computational tasks. In the past decades, there are a lot of variations in perceiving the importance of parallelism in computing machines. And it is observed that the parallel computing is a superior solution to many of the computing limitations like speed and density; non-recurring and high cost; and power consumption and heat dissipation etc. The commercial multiprocessors have emerged with lower prices than the mainframe machines and supercomputers machines. In this article the high performance computing (HPC) through parallel programming paradigms (PPPs) are discussed with their constructs and design approaches.
HOMOGENEOUS MULTISTAGE ARCHITECTURE FOR REAL-TIME IMAGE PROCESSINGcscpconf
In this article, we present a new multistage architecture oriented to real-time complex processing applications. Given a set of rules, this proposed architecture allows the using of different communication links (point to point link, hardware router…) to connect unlimited number of parallel computing elements (software processors) to follow the increasing complexity of algorithms. In particular, this work brings out a parallel implementation of multihypothesis approach for road recognition application on the proposed Multiprocessor Systemon-Chip (MP-SoC) architecture. This algorithm is usually the main part of the lane keeping applications. Experimental results using images of a real road scene are presented. Using a low cost FPGA-based System-on-Chip, our hardware architecture is able to detect and recognize the roadsides in a time limit of 60 mSec. Moreover, we demonstrate that our multistage architecture may be used to achieve good speed-up in solving automotive applications.
A NOVEL METHODOLOGY FOR TASK DISTRIBUTION IN HETEROGENEOUS RECONFIGURABLE COM...ijesajournal
Modern embedded systems are being modeled as Heterogeneous Reconfigurable Computing Systems
(HRCS) where Reconfigurable Hardware i.e. Field Programmable Gate Array (FPGA) and soft core
processors acts as computing elements. So, an efficient task distribution methodology is essential for
obtaining high performance in modern embedded systems. In this paper, we present a novel methodology
for task distribution called Minimum Laxity First (MLF) algorithm that takes the advantage of runtime
reconfiguration of FPGA in order to effectively utilize the available resources. The MLF algorithm is a list
based dynamic scheduling algorithm that uses attributes of tasks as well computing resources as cost
function to distribute the tasks of an application to HRCS. In this paper, an on chip HRCS computing
platform is configured on Virtex 5 FPGA using Xilinx EDK. The real time applications JPEG, OFDM
transmitters are represented as task graph and then the task are distributed, statically as well dynamically,
to the platform HRCS in order to evaluate the performance of the designed task distribution model. Finally,
the performance of MLF algorithm is compared with existing static scheduling algorithms. The comparison
shows that the MLF algorithm outperforms in terms of efficient utilization of resources on chip and also
speedup an application execution.
A NOVEL METHODOLOGY FOR TASK DISTRIBUTION IN HETEROGENEOUS RECONFIGURABLE COM...ijesajournal
Modern embedded systems are being modeled as Heterogeneous Reconfigurable Computing Systems
(HRCS) where Reconfigurable Hardware i.e. Field Programmable Gate Array (FPGA) and soft core
processors acts as computing elements. So, an efficient task distribution methodology is essential for
obtaining high performance in modern embedded systems. In this paper, we present a novel methodology
for task distribution called Minimum Laxity First (MLF) algorithm that takes the advantage of runtime
reconfiguration of FPGA in order to effectively utilize the available resources. The MLF algorithm is a list
based dynamic scheduling algorithm that uses attributes of tasks as well computing resources as cost
function to distribute the tasks of an application to HRCS. In this paper, an on chip HRCS computing
platform is configured on Virtex 5 FPGA using Xilinx EDK. The real time applications JPEG, OFDM
transmitters are represented as task graph and then the task are distributed, statically as well dynamically,
to the platform HRCS in order to evaluate the performance of the designed task distribution model. Finally,
the performance of MLF algorithm is compared with existing static scheduling algorithms. The comparison
shows that the MLF algorithm outperforms in terms of efficient utilization of resources on chip and also
speedup an application execution.
A novel methodology for task distributionijesajournal
Modern embedded systems are being modeled as Heterogeneous Reconfigurable Computing Systems
(HRCS) where Reconfigurable Hardware i.e. Field Programmable Gate Array (FPGA) and soft core
processors acts as computing elements. So, an efficient task distribution methodology is essential for
obtaining high performance in modern embedded systems. In this paper, we present a novel methodology
for task distribution called Minimum Laxity First (MLF) algorithm that takes the advantage of runtime
reconfiguration of FPGA in order to effectively utilize the available resources. The MLF algorithm is a list
based dynamic scheduling algorithm that uses attributes of tasks as well computing resources as cost
function to distribute the tasks of an application to HRCS. In this paper, an on chip HRCS computing
platform is configured on Virtex 5 FPGA using Xilinx EDK. The real time applications JPEG, OFDM
transmitters are represented as task graph and then the task are distributed, statically as well dynamically,
to the platform HRCS in order to evaluate the performance of the designed task distribution model. Finally,
the performance of MLF algorithm is compared with existing static scheduling algorithms. The comparison
shows that the MLF algorithm outperforms in terms of efficient utilization of resources on chip and also
speedup an application execution.
A NOVEL METHODOLOGY FOR TASK DISTRIBUTION IN HETEROGENEOUS RECONFIGURABLE COM...ijesajournal
Modern embedded systems are being modeled as Heterogeneous Reconfigurable Computing Systems
(HRCS) where Reconfigurable Hardware i.e. Field Programmable Gate Array (FPGA) and soft core
processors acts as computing elements. So, an efficient task distribution methodology is essential for
obtaining high performance in modern embedded systems. In this paper, we present a novel methodology
for task distribution called Minimum Laxity First (MLF) algorithm that takes the advantage of runtime
reconfiguration of FPGA in order to effectively utilize the available resources. The MLF algorithm is a list
based dynamic scheduling algorithm that uses attributes of tasks as well computing resources as cost
function to distribute the tasks of an application to HRCS. In this paper, an on chip HRCS computing
platform is configured on Virtex 5 FPGA using Xilinx EDK. The real time applications JPEG, OFDM
transmitters are represented as task graph and then the task are distributed, statically as well dynamically,
to the platform HRCS in order to evaluate the performance of the designed task distribution model. Finally,
the performance of MLF algorithm is compared with existing static scheduling algorithms. The comparison
shows that the MLF algorithm outperforms in terms of efficient utilization of resources on chip and also
speedup an application execution.
A Parallel Computing-a Paradigm to achieve High PerformanceAM Publications
Over last few years there has been rapid changes found in computing field.today, we are using the latest
upgrade system which provides the faster output and high performance. User view towards computing is only to
get the correct and fast result. There are many techniques which improves the system performance. Today’s
widely use computing method is parallel computing. Parallel computing, including foundational and theoretical
aspects, systems, languages, architectures, tools, and applications. It will address all classes of parallelprocessing
platforms including concurrent, multithreaded, multicore, accelerated, multiprocessor, clusters, and
supercomputers. This paper reviews the overview of parallel processing to show how parallel computing can
improve the system performance.
The measure of computerized information being created and put away is expanding at a disturbing rate. This information is classified and handled to distil and convey data to clients crossing various businesses for example, finance, online networking, gaming and so forth. This class of workloads is alluded to as throughput computing applications. Multi-core CPUs have been viewed as reasonable for handling information in such workloads. Be that as it may, energized by high computational throughput and energy proficiency, there has been a fast reception of Graphics Processing Units (GPUs) as computing engines lately. GPU computing has risen lately as a reasonable execution stage for throughput situated applications or regions of code. GPUs began as free units for program execution however there are clear patterns towards tight-sew CPU-GPU integration. In this paper, we look to comprehend cutting edge Heterogeneous System Architecture and inspect a few key segments that influences it to emerge from other architecture designs by analyzing existing inquiries about, articles and reports bearing and future open doors for HSA systems.
STUDY OF VARIOUS FACTORS AFFECTING PERFORMANCE OF MULTI-CORE PROCESSORSijdpsjournal
Advances in Integrated Circuit processing allow for more microprocessor design options. As Chip Multiprocessor system (CMP) become the predominant topology for leading microprocessors, critical components of the system are now integrated on a single chip. This enables sharing of computation resources that was not previously possible. In addition the virtualization of these computation resources exposes the system to a mix of diverse and competing workloads. On chip Cache memory is a resource of primary concern as it can be dominant in controlling overall throughput. This Paper presents analysis of various parameters affecting the performance of Multi-core Architectures like varying the number of cores, changes L2 cache size, further we have varied directory size from 64 to 2048 entries on a 4 node, 8 node 16 node and 64 node Chip multiprocessor which in turn presents an open area of research on multicore processors with private/shared last level cache as the future trend seems to be towards tiled architecture executing multiple parallel applications with optimized silicon area utilization and excellent performance.
BUILDING A PRIVATE HPC CLOUD FOR COMPUTE AND DATA-INTENSIVE APPLICATIONSijccsa
Traditional HPC (High Performance Computing) clusters are best suited for well-formed calculations. The
orderly batch-oriented HPC cluster offers maximal potential for performance per application, but limits
resource efficiency and user flexibility. An HPC cloud can host multiple virtual HPC clusters, giving the
scientists unprecedented flexibility for research and development. With the proper incentive model,
resource efficiency will be automatically maximized. In this context, there are three new challenges. The
first is the virtualization overheads. The second is the administrative complexity for scientists to manage
the virtual clusters. The third is the programming model. The existing HPC programming models were
designed for dedicated homogeneous parallel processors. The HPC cloud is typically heterogeneous and
shared. This paper reports on the practice and experiences in building a private HPC cloud using a subset
of a traditional HPC cluster. We report our evaluation criteria using Open Source software, and
performance studies for compute-intensive and data-intensive applications. We also report the design and
implementation of a Puppet-based virtual cluster administration tool called HPCFY. In addition, we show
that even if the overhead of virtualization is present, efficient scalability for virtual clusters can be achieved
by understanding the effects of virtualization overheads on various types of HPC and Big Data workloads.
We aim at providing a detailed experience report to the HPC community, to ease the process of building a
private HPC cloud using Open Source software.
OpenACC and Open Hackathons Monthly Highlights May 2023.pdfOpenACC
Stay up-to-date on the latest news, research, and resources. This month's edition covers the call for speakers for the Open Accelerated Computing Summit, scheduled Open Hackathons and Bootcamps, an interview with Sunita Chandrasekaran, a call for proposals for the DOE's INCITE program, upcoming webinars, and more!
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
A HYPER-HEURISTIC METHOD FOR SCHEDULING THEJOBS IN CLOUD ENVIRONMENTieijjournal1
Currently cloud computing has turned into a promising technology and has become a great key for
satisfying a flexible service oriented , online provision and storage of computing resources and user’s
information in lesser expense with dynamism framework on pay per use basis.In this technology Job
Scheduling Problem is acritical issue. For well-organizedmanaging and handling resources,
administrations, scheduling plays a vital role. This paper shares out the improved Hyper- Heuristic
Scheduling Approach to schedule resources, by taking account of computation time and makespan with two
detection operators. Operators are used to select the low-level heuristics automatically. Conditional
Revealing Algorithm (CRA)idea is applied for finding the job failures while allocating the resources. We
believe that proposed hyper-heuristic achieve better results than other individual heuristics
A HYPER-HEURISTIC METHOD FOR SCHEDULING THEJOBS IN CLOUD ENVIRONMENTieijjournal
Currently cloud computing has turned into a promising technology and has become a great key for satisfying a flexible service oriented , online provision and storage of computing resources and user’s information in lesser expense with dynamism framework on pay per use basis.In this technology Job Scheduling Problem is acritical issue. For well-organizedmanaging and handling resources, administrations, scheduling plays a vital role. This paper shares out the improved Hyper- Heuristic Scheduling Approach to schedule resources, by taking account of computation time and makespan with two detection operators. Operators are used to select the low-level heuristics automatically. Conditional
Revealing Algorithm (CRA)idea is applied for finding the job failures while allocating the resources. We believe that proposed hyper-heuristic achieve better results than other individual heuristics.
Dominant block guided optimal cache size estimation to maximize ipc of embedd...ijesajournal
Embedded system software is highly constrained from performance, memory footprint, energy consumption and implementing cost view point. It is always desirable to obtain better Instructions per Cycle (IPC). Instruction cache has major contribution in improving IPC. Cache memories are realized on the same chip where the processor is running. This considerably increases the system cost as well. Hence, it is required to maintain a trade-off between cache sizes and performance improvement offered. Determining the number of cache lines and size of cache line are important parameters for cache designing. The design space for cache is quite large. It is time taking to execute the given application with different cache sizes on an instruction set simulator (ISS) to figure out the optimal cache size. In this paper, a technique is proposed to identify a number of cache lines and cache line size for the L1 instruction cache that will offer best or nearly best IPC. Cache size is derived, at a higher abstraction level, from basic block analysis in the Low Level Virtual Machine (LLVM) environment. The cache size estimated from the LLVM environment is cross validated by simulating the set of benchmark applications with different cache sizes in SimpleScalar’s out-of-order simulator. The proposed method seems to be superior in terms of estimation accuracy and/or estimation time as compared to the existing methods for estimation of optimal cache size parameters (cache line size, number of cache lines).
Cloud computing is an emerging technology. It process huge amount of data so scheduling mechanism
works as a vital role in the cloud computing. Thus my protocol is designed to minimize the switching time,
improve the resource utilization and also improve the server performance and throughput. This method or
protocol is based on scheduling the jobs in the cloud and to solve the drawbacks in the existing protocols.
Here we assign the priority to the job which gives better performance to the computer and try my best to
minimize the waiting time and switching time. Best effort has been made to manage the scheduling of jobs
for solving drawbacks of existing protocols and also improvise the efficiency and throughput of the server.
Towards high performance computing(hpc) through parallel programming paradigm...ijpla
Nowadays, we are to find out solutions to huge computing problems very rapidly. It brings the idea of parallel computing in which several machines or processors work cooperatively for computational tasks. In the past decades, there are a lot of variations in perceiving the importance of parallelism in computing machines. And it is observed that the parallel computing is a superior solution to many of the computing limitations like speed and density; non-recurring and high cost; and power consumption and heat dissipation etc. The commercial multiprocessors have emerged with lower prices than the mainframe machines and supercomputers machines. In this article the high performance computing (HPC) through parallel programming paradigms (PPPs) are discussed with their constructs and design approaches.
HOMOGENEOUS MULTISTAGE ARCHITECTURE FOR REAL-TIME IMAGE PROCESSINGcscpconf
In this article, we present a new multistage architecture oriented to real-time complex processing applications. Given a set of rules, this proposed architecture allows the using of different communication links (point to point link, hardware router…) to connect unlimited number of parallel computing elements (software processors) to follow the increasing complexity of algorithms. In particular, this work brings out a parallel implementation of multihypothesis approach for road recognition application on the proposed Multiprocessor Systemon-Chip (MP-SoC) architecture. This algorithm is usually the main part of the lane keeping applications. Experimental results using images of a real road scene are presented. Using a low cost FPGA-based System-on-Chip, our hardware architecture is able to detect and recognize the roadsides in a time limit of 60 mSec. Moreover, we demonstrate that our multistage architecture may be used to achieve good speed-up in solving automotive applications.
A NOVEL METHODOLOGY FOR TASK DISTRIBUTION IN HETEROGENEOUS RECONFIGURABLE COM...ijesajournal
Modern embedded systems are being modeled as Heterogeneous Reconfigurable Computing Systems
(HRCS) where Reconfigurable Hardware i.e. Field Programmable Gate Array (FPGA) and soft core
processors acts as computing elements. So, an efficient task distribution methodology is essential for
obtaining high performance in modern embedded systems. In this paper, we present a novel methodology
for task distribution called Minimum Laxity First (MLF) algorithm that takes the advantage of runtime
reconfiguration of FPGA in order to effectively utilize the available resources. The MLF algorithm is a list
based dynamic scheduling algorithm that uses attributes of tasks as well computing resources as cost
function to distribute the tasks of an application to HRCS. In this paper, an on chip HRCS computing
platform is configured on Virtex 5 FPGA using Xilinx EDK. The real time applications JPEG, OFDM
transmitters are represented as task graph and then the task are distributed, statically as well dynamically,
to the platform HRCS in order to evaluate the performance of the designed task distribution model. Finally,
the performance of MLF algorithm is compared with existing static scheduling algorithms. The comparison
shows that the MLF algorithm outperforms in terms of efficient utilization of resources on chip and also
speedup an application execution.
A NOVEL METHODOLOGY FOR TASK DISTRIBUTION IN HETEROGENEOUS RECONFIGURABLE COM...ijesajournal
Modern embedded systems are being modeled as Heterogeneous Reconfigurable Computing Systems
(HRCS) where Reconfigurable Hardware i.e. Field Programmable Gate Array (FPGA) and soft core
processors acts as computing elements. So, an efficient task distribution methodology is essential for
obtaining high performance in modern embedded systems. In this paper, we present a novel methodology
for task distribution called Minimum Laxity First (MLF) algorithm that takes the advantage of runtime
reconfiguration of FPGA in order to effectively utilize the available resources. The MLF algorithm is a list
based dynamic scheduling algorithm that uses attributes of tasks as well computing resources as cost
function to distribute the tasks of an application to HRCS. In this paper, an on chip HRCS computing
platform is configured on Virtex 5 FPGA using Xilinx EDK. The real time applications JPEG, OFDM
transmitters are represented as task graph and then the task are distributed, statically as well dynamically,
to the platform HRCS in order to evaluate the performance of the designed task distribution model. Finally,
the performance of MLF algorithm is compared with existing static scheduling algorithms. The comparison
shows that the MLF algorithm outperforms in terms of efficient utilization of resources on chip and also
speedup an application execution.
A novel methodology for task distributionijesajournal
Modern embedded systems are being modeled as Heterogeneous Reconfigurable Computing Systems
(HRCS) where Reconfigurable Hardware i.e. Field Programmable Gate Array (FPGA) and soft core
processors acts as computing elements. So, an efficient task distribution methodology is essential for
obtaining high performance in modern embedded systems. In this paper, we present a novel methodology
for task distribution called Minimum Laxity First (MLF) algorithm that takes the advantage of runtime
reconfiguration of FPGA in order to effectively utilize the available resources. The MLF algorithm is a list
based dynamic scheduling algorithm that uses attributes of tasks as well computing resources as cost
function to distribute the tasks of an application to HRCS. In this paper, an on chip HRCS computing
platform is configured on Virtex 5 FPGA using Xilinx EDK. The real time applications JPEG, OFDM
transmitters are represented as task graph and then the task are distributed, statically as well dynamically,
to the platform HRCS in order to evaluate the performance of the designed task distribution model. Finally,
the performance of MLF algorithm is compared with existing static scheduling algorithms. The comparison
shows that the MLF algorithm outperforms in terms of efficient utilization of resources on chip and also
speedup an application execution.
A NOVEL METHODOLOGY FOR TASK DISTRIBUTION IN HETEROGENEOUS RECONFIGURABLE COM...ijesajournal
Modern embedded systems are being modeled as Heterogeneous Reconfigurable Computing Systems
(HRCS) where Reconfigurable Hardware i.e. Field Programmable Gate Array (FPGA) and soft core
processors acts as computing elements. So, an efficient task distribution methodology is essential for
obtaining high performance in modern embedded systems. In this paper, we present a novel methodology
for task distribution called Minimum Laxity First (MLF) algorithm that takes the advantage of runtime
reconfiguration of FPGA in order to effectively utilize the available resources. The MLF algorithm is a list
based dynamic scheduling algorithm that uses attributes of tasks as well computing resources as cost
function to distribute the tasks of an application to HRCS. In this paper, an on chip HRCS computing
platform is configured on Virtex 5 FPGA using Xilinx EDK. The real time applications JPEG, OFDM
transmitters are represented as task graph and then the task are distributed, statically as well dynamically,
to the platform HRCS in order to evaluate the performance of the designed task distribution model. Finally,
the performance of MLF algorithm is compared with existing static scheduling algorithms. The comparison
shows that the MLF algorithm outperforms in terms of efficient utilization of resources on chip and also
speedup an application execution.
A Parallel Computing-a Paradigm to achieve High PerformanceAM Publications
Over last few years there has been rapid changes found in computing field.today, we are using the latest
upgrade system which provides the faster output and high performance. User view towards computing is only to
get the correct and fast result. There are many techniques which improves the system performance. Today’s
widely use computing method is parallel computing. Parallel computing, including foundational and theoretical
aspects, systems, languages, architectures, tools, and applications. It will address all classes of parallelprocessing
platforms including concurrent, multithreaded, multicore, accelerated, multiprocessor, clusters, and
supercomputers. This paper reviews the overview of parallel processing to show how parallel computing can
improve the system performance.
The measure of computerized information being created and put away is expanding at a disturbing rate. This information is classified and handled to distil and convey data to clients crossing various businesses for example, finance, online networking, gaming and so forth. This class of workloads is alluded to as throughput computing applications. Multi-core CPUs have been viewed as reasonable for handling information in such workloads. Be that as it may, energized by high computational throughput and energy proficiency, there has been a fast reception of Graphics Processing Units (GPUs) as computing engines lately. GPU computing has risen lately as a reasonable execution stage for throughput situated applications or regions of code. GPUs began as free units for program execution however there are clear patterns towards tight-sew CPU-GPU integration. In this paper, we look to comprehend cutting edge Heterogeneous System Architecture and inspect a few key segments that influences it to emerge from other architecture designs by analyzing existing inquiries about, articles and reports bearing and future open doors for HSA systems.
STUDY OF VARIOUS FACTORS AFFECTING PERFORMANCE OF MULTI-CORE PROCESSORSijdpsjournal
Advances in Integrated Circuit processing allow for more microprocessor design options. As Chip Multiprocessor system (CMP) become the predominant topology for leading microprocessors, critical components of the system are now integrated on a single chip. This enables sharing of computation resources that was not previously possible. In addition the virtualization of these computation resources exposes the system to a mix of diverse and competing workloads. On chip Cache memory is a resource of primary concern as it can be dominant in controlling overall throughput. This Paper presents analysis of various parameters affecting the performance of Multi-core Architectures like varying the number of cores, changes L2 cache size, further we have varied directory size from 64 to 2048 entries on a 4 node, 8 node 16 node and 64 node Chip multiprocessor which in turn presents an open area of research on multicore processors with private/shared last level cache as the future trend seems to be towards tiled architecture executing multiple parallel applications with optimized silicon area utilization and excellent performance.
BUILDING A PRIVATE HPC CLOUD FOR COMPUTE AND DATA-INTENSIVE APPLICATIONSijccsa
Traditional HPC (High Performance Computing) clusters are best suited for well-formed calculations. The
orderly batch-oriented HPC cluster offers maximal potential for performance per application, but limits
resource efficiency and user flexibility. An HPC cloud can host multiple virtual HPC clusters, giving the
scientists unprecedented flexibility for research and development. With the proper incentive model,
resource efficiency will be automatically maximized. In this context, there are three new challenges. The
first is the virtualization overheads. The second is the administrative complexity for scientists to manage
the virtual clusters. The third is the programming model. The existing HPC programming models were
designed for dedicated homogeneous parallel processors. The HPC cloud is typically heterogeneous and
shared. This paper reports on the practice and experiences in building a private HPC cloud using a subset
of a traditional HPC cluster. We report our evaluation criteria using Open Source software, and
performance studies for compute-intensive and data-intensive applications. We also report the design and
implementation of a Puppet-based virtual cluster administration tool called HPCFY. In addition, we show
that even if the overhead of virtualization is present, efficient scalability for virtual clusters can be achieved
by understanding the effects of virtualization overheads on various types of HPC and Big Data workloads.
We aim at providing a detailed experience report to the HPC community, to ease the process of building a
private HPC cloud using Open Source software.
OpenACC and Open Hackathons Monthly Highlights May 2023.pdfOpenACC
Stay up-to-date on the latest news, research, and resources. This month's edition covers the call for speakers for the Open Accelerated Computing Summit, scheduled Open Hackathons and Bootcamps, an interview with Sunita Chandrasekaran, a call for proposals for the DOE's INCITE program, upcoming webinars, and more!
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
A HYPER-HEURISTIC METHOD FOR SCHEDULING THEJOBS IN CLOUD ENVIRONMENTieijjournal1
Currently cloud computing has turned into a promising technology and has become a great key for
satisfying a flexible service oriented , online provision and storage of computing resources and user’s
information in lesser expense with dynamism framework on pay per use basis.In this technology Job
Scheduling Problem is acritical issue. For well-organizedmanaging and handling resources,
administrations, scheduling plays a vital role. This paper shares out the improved Hyper- Heuristic
Scheduling Approach to schedule resources, by taking account of computation time and makespan with two
detection operators. Operators are used to select the low-level heuristics automatically. Conditional
Revealing Algorithm (CRA)idea is applied for finding the job failures while allocating the resources. We
believe that proposed hyper-heuristic achieve better results than other individual heuristics
A HYPER-HEURISTIC METHOD FOR SCHEDULING THEJOBS IN CLOUD ENVIRONMENTieijjournal
Currently cloud computing has turned into a promising technology and has become a great key for satisfying a flexible service oriented , online provision and storage of computing resources and user’s information in lesser expense with dynamism framework on pay per use basis.In this technology Job Scheduling Problem is acritical issue. For well-organizedmanaging and handling resources, administrations, scheduling plays a vital role. This paper shares out the improved Hyper- Heuristic Scheduling Approach to schedule resources, by taking account of computation time and makespan with two detection operators. Operators are used to select the low-level heuristics automatically. Conditional
Revealing Algorithm (CRA)idea is applied for finding the job failures while allocating the resources. We believe that proposed hyper-heuristic achieve better results than other individual heuristics.
Dominant block guided optimal cache size estimation to maximize ipc of embedd...ijesajournal
Embedded system software is highly constrained from performance, memory footprint, energy consumption and implementing cost view point. It is always desirable to obtain better Instructions per Cycle (IPC). Instruction cache has major contribution in improving IPC. Cache memories are realized on the same chip where the processor is running. This considerably increases the system cost as well. Hence, it is required to maintain a trade-off between cache sizes and performance improvement offered. Determining the number of cache lines and size of cache line are important parameters for cache designing. The design space for cache is quite large. It is time taking to execute the given application with different cache sizes on an instruction set simulator (ISS) to figure out the optimal cache size. In this paper, a technique is proposed to identify a number of cache lines and cache line size for the L1 instruction cache that will offer best or nearly best IPC. Cache size is derived, at a higher abstraction level, from basic block analysis in the Low Level Virtual Machine (LLVM) environment. The cache size estimated from the LLVM environment is cross validated by simulating the set of benchmark applications with different cache sizes in SimpleScalar’s out-of-order simulator. The proposed method seems to be superior in terms of estimation accuracy and/or estimation time as compared to the existing methods for estimation of optimal cache size parameters (cache line size, number of cache lines).
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
1. A REVIEW ON PARALLEL COMPUTING
Wahida Banu1
, Dr.Nandini.N2
1
Reasearch Scholar,VVIET, VTU,Dr .AIT Research Centre.Bangalore
wahidanisar@gmail.com
2
Associate Professor,Guide,VTU,Dr.AIT Bangalore
nandu_8449@rediffmail.com
Abstract.
Parallel computing has become an essential subject in the field of computer science and also
it is shown to be critical when researching in high end solutions. The evolution of computer
architectures (multicore and manycore) towards an increased quantity of cores, where
parallelism could be the approach to option for speeding up an algorithm within the last few
decades, the graphics processing unit, GPU and CPU, has gained an essential place in the
area of high end computing (HPC) due to its low priced and massive processing power that is
parallel. In this paper, we survey the idea of parallel computing, especially CPU computing
and its programming models and also gives a couple of theoretical and technical concepts
which can be often needed to understand the CPU and GPU as well as its parallelism in
massive model. In particular, we show how this technology is new in assisting the field of
computational physics, especially when the issue is data parallel.
Keywords: distributed memory, shared memory, OpenCL, Pthreads, UPC, Fortress OpenMP, MPI, CUDA,
1 Introduction
The reason for synchronous computing is dependable to develop an application execution by
playing out the application frame sort on various processors. While synchronous computing
is usually for this HPC performance that is [high] community, it's becoming more prevalent
in the mainstream computing as a consequence of the present growth of commodity
architecture called multicore. The architecture that is multi-user and quickly many-core, is a
brand name paradigm called new maintain utilizing the Moore's enactment. It is spurred by
the difficulties to worldwide in standard for expanding recurrence called CPU genuine
impediment of transistors measure, vitality utilizes, additionally temperature dispersal [1,2].
Therefore, it is relied upon that eras to happen to applications would enormously abuse the
parallelism offered by the design, multi center engineering. There are two essential
fundamental practices to parallelize application parallelization and synchronous development
that is parallel they differ based on the execution and accommodation of parallelization. The
auto-parallelization approach, e.g. ILP (guideline degree parallelism) or parallel compilers
[3], straight away parallelizes applications that have been produced utilizing consecutive
advancement. The power with this strategy would be the way that current/inheritance
applications won't need end up plainly changed, e.g. applications should be recompiled by
having a parallel compiler. Consequently, programers will maybe not need certain to know
the growth of fresh. But, and this also can become a factor that will be limiting exploiting
an elevated amount of parallelism, it is extremely challenging to automatically transform
algorithms insurance firms a nature that is sequential people that are synchronous. As
opposed to auto parallelization, making utilization of the development that is parallel,
applications are exceptionally created to misuse parallelism. Extensively, having an
application that is parallel parceling workload into undertakings and the mapping of
assignments into specialists. Synchronous writing computer programs apparently impacts an
aggregate result of more noteworthy execution pick up than vehicle parallelization, yet amid
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2. the cost of more parallelization endeavors. In this paper, we describe seven criteria that are
qualitative parallel developed. Our work objective is usually to emphasize the utilization
parallelism that regardless of performance of resulted applications, we provide a research of
six development that is parallel into the community that is HPC three well-established
models (this basically means. OpenMP [6,7], Pthreads [5], and [8] that is MPI and three
models that are relatively new this basically means. UPC [9,10], Fortress [11, 12], and
CUDA.
2. Seven criteria in reviewing the parallel computing architecture
We consider two structures- conveyed memory and gave memory. Given memory design
relates to frameworks such as a SMP/MPP node whereby all processors share an address lone
space. With such models, applications can simply run and utilize processors in negligible a
singular node. So, dispersed memory design relates to frameworks, including a joined
number of process hubs whereby there is one target space for every node .
Fig1: Supported System Architecture / Six Programming Models
Fig. 1 delineates the gadget that is upheld with this six improvement model. As can be seen,
Pthreads, and CUDA, OpenMP help share memory design, in this way can simply just run
and utilize processors in just a node that is lone. That being said, MPI, UPC and Fortress
additionally help distributed memory architecture to ensure that applications developed with
these models can are powered by solitary node (this basically means. Provided memory
architecture) or nodes that are numerous.
Programming Methodologies
We consider precisely how parallelism abilities have problems with programmers. For
examples, API, unique directives, brand name language that is new, etc.
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3. Worker Management
This demands talks in relation to the creation of the device of worker, threads or processes.
Worker management is implicit if no value is had by code writers to handle the level of
workers. Rather, they need to simply specify, for instance, the real range item of workers
required or the positioning of guideline become run in parallel. In explicit approach,
programmer requires to code the destruction and creation of workers.
Workload Partitioning Scheme
Worker partitioning describes the real method workload are divided into smaller chunks
called tasks. In implicit approach, typically programmers need to simply specify that the
workload could be processed in possibly synchronous. What kind of workload is clearly
partitioned into tasks will not need to become managed by coders. In comparison, along side
the programmer's approach being explicit undoubtedly to manually determine exactly how
function stack is divided.
Assignment to aborer Mapping
Assignment to aborer mapping characterizes exactly how undertakings are delineated
specialists. Straightforwardly into the verifiable approach programers won't have any desire
to determine which specialist is accountable for the work that is sure. In correlation, the
programmer's approach which is express control unequivocally how undertakings are doled
out to laborers.
Synchronization
Synchronization portrays the lucky time that is reasonable through which specialists get to
provide information. During verifiable synchronization, there unmistakably was no or
advancement that is little completed by programers either no synchronization develops are
essential or it is really sufficient to simply determine that the synchronization will likely be
fundamental. In unequivocal synchronization, programers require really to address the
specialist's utilization of the provided.
Communication Model
The interaction is covered by this aspect paradigm utilized by way of a model
3. The fundamental difference between CPU and GPU architectures
Contemporary CPUs have actually developed towards synchronous processing, applying the
MIMD architecture. A lot of their die area is reserved for control devices and cache, making
an area that is tiny the computations that are numb. It is because, A central processing unit
carries out such various tasks that having advanced level cache and control mechanisms will
be the method that is only attained a regular performance that is very good. One of the key
objectives regarding the GPU architecture should be to attain the performance that is a
higher parallelism that is massive. In contrast to your Central Processing Unit, the die area
with this GPU is especially occupied by ALUs and an area that is minimal reserved for
control and cache Figure 2: The GPU architecture varies through the Central Processing Unit
because its design is committed to putting numerous tiny cores, offering a space that is less
control and cache devices.
This huge difference in architecture carries a consequence that is direct the GPU is more
restrictive when compared to Central Processing Unit nonetheless it is just a lot that is
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4. wholly effective in case choice could be very carefully created for it. Latest GPU
architectures such as Nvidia’s Fermi and Kepler have actually added an essential degree of
freedom by including a cache that is l2 managing memory that is irregular and in addition by
boosting the performance of atomic operations. But, this freedom remains definitely not
frequently, usually the one contained in CPUs. Indeed, there is a trade-off between power
and flexibility that is computed. Real CPUs challenge to help keep a balance between
computing power and function that is basic while GPUs aim at massive synchronous
arithmetic computations, launching restrictions that are many. Many of these limitations are
overcome through the execution period, though some other people must certainly be
addressed if the nagging issue was parallelized. Most commonly it is wise to have a way of
creating an algorithm that is parallel.
Fig2: The difference CPU and GPU architecture.
Parallel Programming Model
In this part, we assess six developments that is parallel utilizing the requirements presented
in part 2. The summary that is overall shown in Table 1. Assessment of Six Synchronous
Programming Models
OpenMP
OpenMP is a specification that is available memory that is provided [6,7]. It comprises of
musical organization of compiler mandates, callable runtime gathering schedules and
condition aspects that expansion Fortran, C and C++ programs. OpenMP is convenient
through the provided memory engineering. The result of specialists in OpenMP is strings.
The laborer administration is verifiable. Unique mandates are acclimatized to indicate that the
correct piece of rule is kept running in parallel. The sum that is the aggregate of to be utilized
is indicated utilizing an out-of-band action which is a rearing ground movable. Therefore, not
at all like tread, there's no need for coders to change the level of strings. Workload
partitioning and task-to-worker mapping desire a development that is somewhat few simply
specifying compiler directives to denote a synchronous area, especially
(i) pragma unparalleled for C/C++, and
(ii) omp parallel and amp end parallel for Fortran.
OpenMP furthermore abstracts away precisely how workload (a selection) is split into tasks
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5. (sub-arrays) and the way in which tasks are assigned to threads.
OpenMP supports constructs being a few assistance synchronization called implicit
programers specify simply where synchronization occurs (Table 2). The synchronization that
is actually is ergo relieved through the code writers’ responsibility
Portable working System Interface) or P Threads is only a couple of C program dialect that
is composed and technique calls [5]. P Threads are executed turned into a header (trad. h)
And a gathering for making and controlling almost the greater part of the specialists called
worker administration in p threads expects a software engineer to make and annihilate
evidently strings by simply influencing utilization of it to work that is tried. Capacity Pthread
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6. makes requires four parameters: (i) the string helpful to influence utilization of undertakings,
(ii) to property, (iii) errands progress toward becoming keep running by string in routine call,
and (iv) contention that is normal. The string created will run the routine until the p thread leave work is
named.
Workload errand and dividing mapping are obviously determined by programmers as
continuations to tread make. The workload apportioning is indicated by coders concerning
the passing that is third by the methods for the call that is normal while errand mapping is
determined on the passing that is beginning into the threads make work. A string can join
different strings using join that is tried. After the function is named, the string that is calling
hold its execution before the objective string complete before joining the strings.
At whatever point strings that are boundless the provided data, coders ought to be tuned
straightforwardly into giving data fight and stops. To shield segment that is plainly an
absolute necessity to diversely express it. The level of code that gets to share information,
threads give mute (common rejection) and semaphore [13]. The matrix allows only one single
string to enter a section that is an absolute necessity any gave time, though semaphore
permits a string that are few enter the part that is crucial..
2. CUDA (Compute Unified unit Architecture)
CUDA will be the development of C programs, composing dialect worked to help of
synchronous preparing on NVIDIA GPU (Graphics Processing Unit) [12]. CUDA sees a
parallel framework as comprises of a genuine amount unit (this fundamentally
Fig. 3. CUDA Architecture
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7. CPU) and calculation resource (this essentially means. GPU). The calculation of tasks is
completed in GPU insurance firms a couple of of threads that run in parallel. The GPU
architecture for threads consist of a two-level hierarchy, specially block (and grid Fig.3). A
block is a couple of of tightly combined threads where each thread is identified simply by
using a thread ID, while the grid is a Tru quantity of loosely combined of obstructs with
comparable size and measurement.
Worker management in CUDA is completed implicitly; programmers usually do not thread
that is managed and destructions. They need to simply specify the dimension of the grid and
block possessed a need to process a working work that is specific. While workload parti-
tioning and worker mapping in CUDA is completed demonstrably.Programersneed to
ascertain the workload become run in synchronous with the use of worldwide
Function[dimGrid, dimBlock] (Arguments) construct where in ( global Function would be
the worldwide function call become run in threads, (ii) dimGrid could be the dimension and
size for the grid, (iii) dimBlock is the dimension and size of each and every block and (iv)
Arguments represent the going value for the big event that is global. The task to worker map-
ping of CUDA development is defined on [dimGrid, dimBlock]into the command call
discussed earlier.
Open CL
OpenCL™ (open language that is computing are the available, royalty free standard for
cross-platform, synchronous growth of diverse processors discovered in PCs, servers,
cellular devices and embedded platforms. OpenCL significantly improves the purchase price
and responsiveness in relation to the wide variety of applications in plenty of market that is
significantly different games task including, systematic and PC that is medical, expert
revolutionary tools, eyesight processing, and neural community training and influencing.
OpenCL 2.2 brings the OpenCL C++ kernel language towards the core specification for
dramatically improved development efficiency that is parallel
1.OpenCL C++ kernel language is just a subset that is static of C++14 standard and includes
classes, templates, lambda expressions, function overloads and an amount that is greater of
constructs for meta-programming and generic
2.Leverages the maker Khronos that is brand name brand, new language that is SPIR-V is
intermediate completely supporting the OpenCL C++ kernel language
3.OpenCL collection functions are in a posture to utilize the C++ language to create
increased security and repaid behavior that is accessing that is undefined, such as atomics,
iterators, pictures, samplers, pipelines, and device queue integral kinds and target areas
4.Pipe space for preserving is device-side that is totally fresh in OpenCL 2.2 that is of use for
FPGA implementations by simply connectivity that is making and kind grasped at compile
time, allowing device-scope that is efficient between kernels
5.OpenCL 2.2 also contains features for improved optimization of generating guideline:
applications could perhaps provide the worth of specialization constant at SPIR-V
compilation time, a problem that is brand detect that is constructors being brand new
destructors of the system range things that are global and certain callbacks are set at a system
launch time.
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8. 5. MPI
Worker management is performed implicitly whereby it is not required to code the creation,
scheduling, or destruction of procedures. Rather, one just requires to simply take
advantageous asset of the unit called command-line mpirun, to fair share using the MPI
runtime precisely how numerous processes are expected, and optionally the mapping of the
procedures to processors. The runtime infrastructure will more than likely carry the worker
then administer out with regards to users in accordance with this info.
Workload task and partitioning mapping should be done by coders, similar to Pthread.
Programers need certainly to address precisely what tasks become computed by each
procedure. For instance, offered an array that is 2-Di. e. The workload), you are going to use
an operation’ identifier (this essentially means. Ranking) to understand which sub-array
working the duty shall determine. Correspondence among strategies receives the message-
passing worldview where data sharing is performed by one system conveying the data nearby
different methodology. MPI comprehensively classifies its message-passing operations as a
group and point-to-point. Point-to-point operations like the MPI Send/MPI Recv set upgrade
correspondences between methodology, while aggregate operations, for example, MPI Bcast
improve interchanges including an entire numerous more than two systems.
Table 3: description of mechanim and syntax of computing.
MPI Barrier is required to specify that the synchronization shall be necessary. The barrier
procedure obstructs each procedure from continuing its execution until all procedures have
entered the barrier. A use that is typical of should be to make certain that worldwide
information is dispersed to processes that are appropriate
4. Summary
In the last 40 years, parallel computing has evolved significantly from being truly a matter of
high equipped data centers and supercomputers to virtually every digital camera that runs on
the CPU or GPU. Today, the field of parallel computing is having certainly one of its best
moments ever sold of computing and its own importance will simply grow provided that
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9. computer architectures keep evolving up to a higher amount of processors. Using seven
criteria, we now have reviewed the qualitative areas of six representative programming that is
parallel. Our main aim of the paper is to give a guideline that is basic in evaluating the
appropriateness of the programming model in several development environments. The sort is
indicated by the system architecture face of computing infrastructure supported by each one
of the models which are programmed. The residual aspects, which complement the
performance that is typical, are designed to aid users in evaluating the simplicity of use of
models. It must be noted that the operational system architecture is in no way exhaustive. On
the Other hand it describes the implementation issues such as for example debugging support
should be thought about as well when evaluating a programming that is parallel.
References
1. Kish, L.B.: End of Moore´s Law: Thermal (noise) Death of Integration in Micro and nano
electronics. Physics Letters A 305, 144–149 (2002)
2. Kish, L.B.: Moore´s Law and the Energy Requirement of Computing Versus
performance. Circuits, devices and systems 151(2), 190–194 (2004)
3. Sun Studio 12, http://developers.sun.com/sunstudio
4. Asanovic, K., Bodik, R., Catanzaro, B.C., Gebis, J.J., Husbands, P., Keutzer, K.,
Patterson, D.A., Plishker, W.L., Shalf, J., Williams, S.W., Yelick, K.A.: The Landscape
of Parallel Computing Research: a view from Berkeley. Technical Report UCB/EECS-
2006-183, Electrical Engineering and Computer Sciences, University of California at
Berkeley (December 2006)
5. Butenhof, D.R.: Programming with POSIX Threads. Addison-Wesley, Reading (1997)
6. OpenMP, http://www.openmp.org
7. Chapman, B., Jost, G., Van Der Pas, R.: Using OpenMP: Portable Shared Memory
Parallel Programming. MIT Press, Cambridge (2007)
8. Pacheco, P.S.: Parallel Programming with MPI. Morgan Kaufmann, San Francisco (1996)
9. Consortium, U.: UPC Language Specifications, v1.2. Technical report (2005)
10.Husbands, P., Iancu, C., Yelick, K.: A Performance Analysis of the Berkeley UPC
Compiler. In: ICS 2003: Proceedings of the 17th annual international conference on
Supercomputing, pp. 63–73. ACM, New York (2003)
11.Allen, E., Chase, D., Hallett, J., Luchangco, V., Maessen, J.W., Ryu, S., Steele Jr., G.L.,
Tobin-Hochstadt, S.: The Fortress Language Specification Version 1.0 beta. Technical
report (March 2007)
12.Corporation, N.: NVIDIA CUDA Programming Guide, version 1.1. Technical re-port
(November 2007)
13.Grama, A., Karypis, G., Kumar, V., Gupta, A.: Introduction to Parallel Comput-ing, 2nd
edn. Addison-Wesley, Boston (2003)
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 15, No. 10, October 2017
272 https://sites.google.com/site/ijcsis/
ISSN 1947-5500