This document outlines the course policies and contents of an introduction to parallel computing course. The course will cover fundamentals of parallel platforms, parallel programming using message passing and threads, and parallel algorithms. It will introduce concepts like multicore processing, GPGPU computing, and parallel programming models. The course is divided into sections on fundamentals, programming, and algorithms. References for further reading on parallel and distributed computing are also provided.
4 - Simulation and analysis of different DCT techniques on MATLAB (presented ...Youness Lahdili
ย
This document summarizes a student project that simulated and analyzed different discrete cosine transform (DCT) techniques for image compression in MATLAB. The objectives were to implement 1D-DCT computing using different methods like Chen's algorithm and Loeffler's algorithm. The student tested the different DCT implementations and compared their performance in terms of speed and mean squared error. The results showed that the DCT technique designed was feasible in MATLAB and could potentially be optimized and ported to FPGA for applications like image and video compression.
Manufacturers have hit limits for single-core processors due to physical constraints, so parallel processing using multiple smaller cores is now common. The .NET framework includes classes like Task Parallel Library (TPL) and Parallel LINQ (PLINQ) that make it easy to take advantage of multi-core systems while abstracting thread management. TPL allows executing code asynchronously using tasks, which can run in parallel and provide callbacks to handle completion and errors. PLINQ allows parallelizing LINQ queries.
This document discusses accelerating machine learning prediction pipelines by splitting them into optimized stages that can run on different hardware targets like CPUs and FPGAs. The authors implemented a sentiment analysis pipeline in three stages - tokenization and character n-grams, word n-grams, and linear regression - and saw performance improvements from buffer sharing and hardware acceleration. While their approach showed promise, open problems remain in automatically identifying and optimizing stages to better accelerate generic prediction pipelines across different models and hardware.
The document discusses parallel computing over the past 25 years and challenges for using multicore chips in the next decade. It aims to provide context to scale applications effectively to 32-1024 cores. Key challenges include expressing inherent application parallelism while enabling efficient mapping to hardware through programming models and runtime systems. Future work includes developing methods to restore lost parallelism information and tradeoffs between programming effort, generality and performance.
1. The document proposes a flexible hardware architecture for image scaling using a programmable 2D separable convolution engine.
2. It describes how any scaling operation can be decomposed into three steps: anti-aliasing filtering, continuous image reconstruction via convolution, and resampling to the output grid.
3. The proposed architecture uses a memory to store a programmable interpolation kernel and enables different scaling algorithms like nearest neighbor and bicubic interpolation by programming the kernel.
This document discusses the design of a pipelined architecture for sparse matrix-vector multiplication on an FPGA. It begins with introductions to matrices, linear algebra, and matrix multiplication. It then describes the objective of building a hardware processor to perform multiple arithmetic operations in parallel through pipelining. The document reviews literature on pipelined floating point units. It provides details on the proposed pipelined design for sparse matrix-vector multiplication, including storing vector values in on-chip memory and using multiple pipelines to complete results in parallel. Simulation results showing reduced power and execution time are presented before concluding the design can improve performance for scientific applications.
Introduction to Convolutional Neural NetworksParrotAI
ย
This document provides an introduction and overview of convolutional neural networks (CNNs). It discusses the key operations in a CNN including convolution, nonlinearity, pooling, and fully connected layers. Convolution extracts features from input images using small filters that preserve spatial relationships between pixels. Pooling reduces the dimensionality of feature maps. The network is trained end-to-end using backpropagation to update filter weights and minimize errors between predicted and true outputs. Visualizing CNNs helps understand how they learn features from images to perform classification.
This document outlines the course policies and contents of an introduction to parallel computing course. The course will cover fundamentals of parallel platforms, parallel programming using message passing and threads, and parallel algorithms. It will introduce concepts like multicore processing, GPGPU computing, and parallel programming models. The course is divided into sections on fundamentals, programming, and algorithms. References for further reading on parallel and distributed computing are also provided.
4 - Simulation and analysis of different DCT techniques on MATLAB (presented ...Youness Lahdili
ย
This document summarizes a student project that simulated and analyzed different discrete cosine transform (DCT) techniques for image compression in MATLAB. The objectives were to implement 1D-DCT computing using different methods like Chen's algorithm and Loeffler's algorithm. The student tested the different DCT implementations and compared their performance in terms of speed and mean squared error. The results showed that the DCT technique designed was feasible in MATLAB and could potentially be optimized and ported to FPGA for applications like image and video compression.
Manufacturers have hit limits for single-core processors due to physical constraints, so parallel processing using multiple smaller cores is now common. The .NET framework includes classes like Task Parallel Library (TPL) and Parallel LINQ (PLINQ) that make it easy to take advantage of multi-core systems while abstracting thread management. TPL allows executing code asynchronously using tasks, which can run in parallel and provide callbacks to handle completion and errors. PLINQ allows parallelizing LINQ queries.
This document discusses accelerating machine learning prediction pipelines by splitting them into optimized stages that can run on different hardware targets like CPUs and FPGAs. The authors implemented a sentiment analysis pipeline in three stages - tokenization and character n-grams, word n-grams, and linear regression - and saw performance improvements from buffer sharing and hardware acceleration. While their approach showed promise, open problems remain in automatically identifying and optimizing stages to better accelerate generic prediction pipelines across different models and hardware.
The document discusses parallel computing over the past 25 years and challenges for using multicore chips in the next decade. It aims to provide context to scale applications effectively to 32-1024 cores. Key challenges include expressing inherent application parallelism while enabling efficient mapping to hardware through programming models and runtime systems. Future work includes developing methods to restore lost parallelism information and tradeoffs between programming effort, generality and performance.
1. The document proposes a flexible hardware architecture for image scaling using a programmable 2D separable convolution engine.
2. It describes how any scaling operation can be decomposed into three steps: anti-aliasing filtering, continuous image reconstruction via convolution, and resampling to the output grid.
3. The proposed architecture uses a memory to store a programmable interpolation kernel and enables different scaling algorithms like nearest neighbor and bicubic interpolation by programming the kernel.
This document discusses the design of a pipelined architecture for sparse matrix-vector multiplication on an FPGA. It begins with introductions to matrices, linear algebra, and matrix multiplication. It then describes the objective of building a hardware processor to perform multiple arithmetic operations in parallel through pipelining. The document reviews literature on pipelined floating point units. It provides details on the proposed pipelined design for sparse matrix-vector multiplication, including storing vector values in on-chip memory and using multiple pipelines to complete results in parallel. Simulation results showing reduced power and execution time are presented before concluding the design can improve performance for scientific applications.
Introduction to Convolutional Neural NetworksParrotAI
ย
This document provides an introduction and overview of convolutional neural networks (CNNs). It discusses the key operations in a CNN including convolution, nonlinearity, pooling, and fully connected layers. Convolution extracts features from input images using small filters that preserve spatial relationships between pixels. Pooling reduces the dimensionality of feature maps. The network is trained end-to-end using backpropagation to update filter weights and minimize errors between predicted and true outputs. Visualizing CNNs helps understand how they learn features from images to perform classification.
This document provides an overview of parallel computing and parallel processing. It discusses:
1. The three types of concurrent events in parallel processing: parallel, simultaneous, and pipelined events.
2. The five fundamental factors for projecting computer performance: clock rate, cycles per instruction (CPI), execution time, million instructions per second (MIPS) rate, and throughput rate.
3. The four programmatic levels of parallel processing from highest to lowest: job/program level, task/procedure level, interinstruction level, and intrainstruction level.
Inference on edge has an ever increasing performance for companies and thus it is crucial to be able to make models smaller. Compressing models can be loss-less or can result in loss of accuracy. This presentation provides a survey of compression techniques for deep learning models. It then describes different architectures of AWS IoT/Green Grass to combine on-device inference and GPU inference in a hub model. Additionally the presentation introduces MXNet, which has small footprint and efficient both for inference and training in distributed settings.
Detailed Simulation of Large-Scale Wireless NetworksGabriele D'Angelo
ย
WiFra is a new framework for the detailed simulation of very large-scale wireless networks. It is based on the parallel and distributed simulation approach and provides high scalability in terms of size of simulated networks and number of execution units running the simulation. In order to improve the performance of distributed simulation, additional techniques are proposed. Their aim is to reduce the communication overhead and to maintain a good level of load-balancing. Simulation architectures composed of low-cost Commercial-Off-The-Shelf (COTS) hardware are specifically supported by WiFra. The framework dynamically reconfigures the simulation, taking care of the performance of each part of the execution architecture and dealing with unpredictable fluctuations of the available computation power and communication load on the single execution units. A fine-grained model of the 802.11 DCF protocol has been used for the performance evaluation of the proposed framework. The results demonstrate that the distributed approach is suitable for the detailed simulation of very-large scale wireless networks.
Using Multi-layered Feed-forward Neural Network (MLFNN) Architecture as Bidir...IOSR Journals
ย
This document presents a method for using a multi-layered feed-forward neural network (MLFNN) architecture as a bidirectional associative memory (BAM) for function approximation. It proposes applying the backpropagation algorithm in two phases - first in the forward direction, then in the backward direction - which allows the MLFNN to work like a BAM. Simulation results show that this two-phase backpropagation algorithm achieves convergence faster than standard backpropagation when approximating the sine function, demonstrating that the MLFNN architecture is better suited for function approximation when trained this way.
A brief introduction to deep learning, providing rough interpretation to deep neural networks and simple implementations with Keras for deep learning beginners.
Bt0068 computer organization and architecturesmumbahelp
ย
This document provides information about getting fully solved assignments for SMU BSC IT courses. It lists the semester, subject code, credit hours, and BK ID for an example Computer Organization and Architecture assignment. It also provides answers to 6 questions related to microoperations, computer bus structure, instruction formats, ten's complement, memory mapping, and interrupt-driven I/O. Students are instructed to send their semester and specialization to a email address or call a phone number to receive solved assignments.
The document describes a multi-FPGA architecture called DReAMS that allows dynamic reconfiguration across multiple FPGAs. It inherits architectures and tools from an existing DRESD project. The workflow involves VHDL system description, simulation, system creation for a specific architecture, and bitstream creation and download onto FPGAs.
Lecture 4 principles of parallel algorithm design updatedVajira Thambawita
ย
The main principles of parallel algorithm design are discussed here. For more information: visit, https://sites.google.com/view/vajira-thambawita/leaning-materials
Transfer learning with LTANN-MEM & NSA for solving multi-objective symbolic r...Amr Kamel Deklel
ย
Abstract
Long Term Artificial Neural Network Memory (LTANN-MEM) and Neural Symbolization Algorithm (NSA)
are proposed for solving symbolic regression problems. Although this approach is capable of solving Boolean
decoder problems of sizes 6, 11 and 20, it is not capable of solving decoder problems of higher dimensions like
decoder-37; decoder-n is decoder with sum of inputs and outputs is n for example decoder-20 is decoder with 4
inputs and 16 outputs. It is shown here that LTANN-MEM and NSA approach is a kind of transfer learning
however it lacks for sub tasking transfer and updatable LTANN-MEM. An approach for adding the sub tasking
transfer and LTANN-MEM updates is discussed here and examined by solving decoder problems of sizes 37, 70
and 135 efficiently. Comparisons with two learning classifier systems are performed and it is found that the
proposed approach in this work outperforms both of them. It is shown that the proposed approach is used also for
solving decoder-264 efficiently. According to the best of our knowledge, there is no reported approach for solving
this high dimensional problem.
This thesis proposes a design methodology for dynamically reconfigurable multi-FPGA systems. The methodology includes three main phases: design extraction from VHDL, static global layout partitioning and placement, and reuse of blocks through dynamic reconfiguration when needed to minimize delays. The major contribution is a multi-FPGA design flow that exploits dynamic reconfiguration to reuse blocks and reduce the application area requirements. Experimental results show the proposed approaches partition and place designs efficiently. Future work includes improving clustering metrics, routing algorithms, and time estimation for dynamic block reuse.
Keras with Tensorflow backend can be used for neural networks and deep learning in both R and Python. The document discusses using Keras to build neural networks from scratch on MNIST data, using pre-trained models like VGG16 for computer vision tasks, and fine-tuning pre-trained models on limited data. Examples are provided for image classification, feature extraction, and calculating image similarities.
A tutorial on CGAL polyhedron for subdivision algorithmsRadu Ursu
ย
This document provides a tutorial on implementing subdivision algorithms using the CGAL polyhedron data structure. It summarizes two approaches for subdivision: using Euler operators for โ3 subdivision and a modifier callback mechanism for quad-triangle subdivision. It then introduces a combinatorial subdivision library (CSL) with increased abstraction, demonstrating Catmull-Clark and Doo-Sabin subdivisions. Accompanying applications visualize the subdivision schemes and provide interaction capabilities. The goal is to demonstrate connectivity and geometry operations on CGAL polyhedra in the context of subdivision algorithms.
2017 (albawi-alkabi)image-net classification with deep convolutional neural n...ali hassan
ย
The document describes a study that trained a large, deep convolutional neural network to classify images in the ImageNet dataset. The network achieved top-1 and top-5 error rates of 37.5% and 17.0% respectively, outperforming previous methods. Key aspects of the network included the use of ReLU activations, dropout regularization, and multiple GPUs for training the large model.
Convolutional neural network from VGG to DenseNetSungminYou
ย
This document summarizes recent developments in convolutional neural networks (CNNs) for image recognition, including residual networks (ResNets) and densely connected convolutional networks (DenseNets). It reviews CNN structure and components like convolution, pooling, and ReLU. ResNets address degradation problems in deep networks by introducing identity-based skip connections. DenseNets connect each layer to every other layer to encourage feature reuse, addressing vanishing gradients. The document outlines the structures of ResNets and DenseNets and their advantages over traditional CNNs.
Enhancing the matrix transpose operation using intel avx instruction set exte...ijcsit
ย
General-purpose microprocessors are augmented with short-vector instruction extensions in order to
simultaneously process more than one data element using the same operation. This type of parallelism is
known as data-parallel processing. Many scientific, engineering, and signal processing applications can be
formulated as matrix operations. Therefore, accelerating these kernel operations on microprocessors,
which are the building blocks or large high-performance computing systems, will definitely boost the
performance of the aforementioned applications. In this paper, we consider the acceleration of the matrix
transpose operation using the 256-bit Intel advanced vector extension (AVX) instructions. We present a
novel vector-based matrix transpose algorithm and its optimized implementation using AVX instructions.
The experimental results on Intel Core i7 processor demonstrates a 2.83 speedup over the standard
sequential implementation, and a maximum of 1.53 speedup over the GCC library implementation. When
the transpose is combined with matrix addition to compute the matrix update, B + AT, where A and B are
squared matrices, the speedup of our implementation over the sequential algorithm increased to 3.19.
This document discusses patterns for parallel computing. It outlines key concepts like Amdahl's law and types of parallelism like data and task parallelism. Examples are provided of how major tech companies like Microsoft, Google, Amazon implement parallelism at different levels of their infrastructure and applications to scale efficiently. Design principles are discussed for converting sequential programs to parallel programs while maintaining performance.
Keras is a high-level neural networks API, written in Python and capable of running on top of either TensorFlow, CNTK or Theano.
We can easily build a model and train it using keras very easily with few lines of code.The steps to train the model is described in the presentation.
Use Keras if you need a deep learning library that:
-Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility).
-Supports both convolutional networks and recurrent networks, as well as combinations of the two.
-Runs seamlessly on CPU and GPU.
The theory behind parallel computing is covered here. For more theoretical knowledge: https://sites.google.com/view/vajira-thambawita/leaning-materials
This document discusses multivector and SIMD computers. It covers vector processing principles including vector instruction types like vector-vector, vector-scalar, and vector-memory instructions. It also discusses compound vector operations, vector loops and chaining. Finally, it discusses SIMD computer implementation models like distributed and shared memory, and SIMD instruction types.
Introduction to Segmentation in Computer vision ParrotAI
ย
Semantic segmentation is a dense prediction task that labels each pixel of an image with a class. It has applications in autonomous vehicles, medical imaging, and surgeries. Popular architectures for semantic segmentation include U-Net, which uses an encoder-decoder structure with skip connections, and Tiramisu, which uses dense blocks. The loss function commonly used is pixel-wise cross entropy loss, which examines predictions at each pixel.
Computer Architecture presentation covers topics like pipelining, VLIW architecture, and loop optimizations. Pipelining allows storing and executing instructions in an orderly process by dividing the instruction cycle into stages. VLIW was invented by Josh Fisher in the 1980s and breaks instructions into basic operations that can execute in parallel. Pipeline scheduling is used to run pipelines at regular intervals and has benefits for continuous integration like automating recurring tasks. Loop unrolling attempts to minimize loop overhead by manually expanding the loop body multiple times.
This document proposes extending algorithmic skeletons with event-driven programming to address the inversion of control problem in skeleton frameworks. It introduces event listeners that can be registered at event hooks within skeletons to access runtime information. This allows implementing non-functional concerns like logging and performance monitoring separately from the core parallel logic. The approach is implemented in the Skandium skeleton library, and examples are given of a logger and online performance monitor built using it. An analysis shows the overhead of processing events is negligible, at around 20 microseconds per event.
This document provides an overview of parallel computing and parallel processing. It discusses:
1. The three types of concurrent events in parallel processing: parallel, simultaneous, and pipelined events.
2. The five fundamental factors for projecting computer performance: clock rate, cycles per instruction (CPI), execution time, million instructions per second (MIPS) rate, and throughput rate.
3. The four programmatic levels of parallel processing from highest to lowest: job/program level, task/procedure level, interinstruction level, and intrainstruction level.
Inference on edge has an ever increasing performance for companies and thus it is crucial to be able to make models smaller. Compressing models can be loss-less or can result in loss of accuracy. This presentation provides a survey of compression techniques for deep learning models. It then describes different architectures of AWS IoT/Green Grass to combine on-device inference and GPU inference in a hub model. Additionally the presentation introduces MXNet, which has small footprint and efficient both for inference and training in distributed settings.
Detailed Simulation of Large-Scale Wireless NetworksGabriele D'Angelo
ย
WiFra is a new framework for the detailed simulation of very large-scale wireless networks. It is based on the parallel and distributed simulation approach and provides high scalability in terms of size of simulated networks and number of execution units running the simulation. In order to improve the performance of distributed simulation, additional techniques are proposed. Their aim is to reduce the communication overhead and to maintain a good level of load-balancing. Simulation architectures composed of low-cost Commercial-Off-The-Shelf (COTS) hardware are specifically supported by WiFra. The framework dynamically reconfigures the simulation, taking care of the performance of each part of the execution architecture and dealing with unpredictable fluctuations of the available computation power and communication load on the single execution units. A fine-grained model of the 802.11 DCF protocol has been used for the performance evaluation of the proposed framework. The results demonstrate that the distributed approach is suitable for the detailed simulation of very-large scale wireless networks.
Using Multi-layered Feed-forward Neural Network (MLFNN) Architecture as Bidir...IOSR Journals
ย
This document presents a method for using a multi-layered feed-forward neural network (MLFNN) architecture as a bidirectional associative memory (BAM) for function approximation. It proposes applying the backpropagation algorithm in two phases - first in the forward direction, then in the backward direction - which allows the MLFNN to work like a BAM. Simulation results show that this two-phase backpropagation algorithm achieves convergence faster than standard backpropagation when approximating the sine function, demonstrating that the MLFNN architecture is better suited for function approximation when trained this way.
A brief introduction to deep learning, providing rough interpretation to deep neural networks and simple implementations with Keras for deep learning beginners.
Bt0068 computer organization and architecturesmumbahelp
ย
This document provides information about getting fully solved assignments for SMU BSC IT courses. It lists the semester, subject code, credit hours, and BK ID for an example Computer Organization and Architecture assignment. It also provides answers to 6 questions related to microoperations, computer bus structure, instruction formats, ten's complement, memory mapping, and interrupt-driven I/O. Students are instructed to send their semester and specialization to a email address or call a phone number to receive solved assignments.
The document describes a multi-FPGA architecture called DReAMS that allows dynamic reconfiguration across multiple FPGAs. It inherits architectures and tools from an existing DRESD project. The workflow involves VHDL system description, simulation, system creation for a specific architecture, and bitstream creation and download onto FPGAs.
Lecture 4 principles of parallel algorithm design updatedVajira Thambawita
ย
The main principles of parallel algorithm design are discussed here. For more information: visit, https://sites.google.com/view/vajira-thambawita/leaning-materials
Transfer learning with LTANN-MEM & NSA for solving multi-objective symbolic r...Amr Kamel Deklel
ย
Abstract
Long Term Artificial Neural Network Memory (LTANN-MEM) and Neural Symbolization Algorithm (NSA)
are proposed for solving symbolic regression problems. Although this approach is capable of solving Boolean
decoder problems of sizes 6, 11 and 20, it is not capable of solving decoder problems of higher dimensions like
decoder-37; decoder-n is decoder with sum of inputs and outputs is n for example decoder-20 is decoder with 4
inputs and 16 outputs. It is shown here that LTANN-MEM and NSA approach is a kind of transfer learning
however it lacks for sub tasking transfer and updatable LTANN-MEM. An approach for adding the sub tasking
transfer and LTANN-MEM updates is discussed here and examined by solving decoder problems of sizes 37, 70
and 135 efficiently. Comparisons with two learning classifier systems are performed and it is found that the
proposed approach in this work outperforms both of them. It is shown that the proposed approach is used also for
solving decoder-264 efficiently. According to the best of our knowledge, there is no reported approach for solving
this high dimensional problem.
This thesis proposes a design methodology for dynamically reconfigurable multi-FPGA systems. The methodology includes three main phases: design extraction from VHDL, static global layout partitioning and placement, and reuse of blocks through dynamic reconfiguration when needed to minimize delays. The major contribution is a multi-FPGA design flow that exploits dynamic reconfiguration to reuse blocks and reduce the application area requirements. Experimental results show the proposed approaches partition and place designs efficiently. Future work includes improving clustering metrics, routing algorithms, and time estimation for dynamic block reuse.
Keras with Tensorflow backend can be used for neural networks and deep learning in both R and Python. The document discusses using Keras to build neural networks from scratch on MNIST data, using pre-trained models like VGG16 for computer vision tasks, and fine-tuning pre-trained models on limited data. Examples are provided for image classification, feature extraction, and calculating image similarities.
A tutorial on CGAL polyhedron for subdivision algorithmsRadu Ursu
ย
This document provides a tutorial on implementing subdivision algorithms using the CGAL polyhedron data structure. It summarizes two approaches for subdivision: using Euler operators for โ3 subdivision and a modifier callback mechanism for quad-triangle subdivision. It then introduces a combinatorial subdivision library (CSL) with increased abstraction, demonstrating Catmull-Clark and Doo-Sabin subdivisions. Accompanying applications visualize the subdivision schemes and provide interaction capabilities. The goal is to demonstrate connectivity and geometry operations on CGAL polyhedra in the context of subdivision algorithms.
2017 (albawi-alkabi)image-net classification with deep convolutional neural n...ali hassan
ย
The document describes a study that trained a large, deep convolutional neural network to classify images in the ImageNet dataset. The network achieved top-1 and top-5 error rates of 37.5% and 17.0% respectively, outperforming previous methods. Key aspects of the network included the use of ReLU activations, dropout regularization, and multiple GPUs for training the large model.
Convolutional neural network from VGG to DenseNetSungminYou
ย
This document summarizes recent developments in convolutional neural networks (CNNs) for image recognition, including residual networks (ResNets) and densely connected convolutional networks (DenseNets). It reviews CNN structure and components like convolution, pooling, and ReLU. ResNets address degradation problems in deep networks by introducing identity-based skip connections. DenseNets connect each layer to every other layer to encourage feature reuse, addressing vanishing gradients. The document outlines the structures of ResNets and DenseNets and their advantages over traditional CNNs.
Enhancing the matrix transpose operation using intel avx instruction set exte...ijcsit
ย
General-purpose microprocessors are augmented with short-vector instruction extensions in order to
simultaneously process more than one data element using the same operation. This type of parallelism is
known as data-parallel processing. Many scientific, engineering, and signal processing applications can be
formulated as matrix operations. Therefore, accelerating these kernel operations on microprocessors,
which are the building blocks or large high-performance computing systems, will definitely boost the
performance of the aforementioned applications. In this paper, we consider the acceleration of the matrix
transpose operation using the 256-bit Intel advanced vector extension (AVX) instructions. We present a
novel vector-based matrix transpose algorithm and its optimized implementation using AVX instructions.
The experimental results on Intel Core i7 processor demonstrates a 2.83 speedup over the standard
sequential implementation, and a maximum of 1.53 speedup over the GCC library implementation. When
the transpose is combined with matrix addition to compute the matrix update, B + AT, where A and B are
squared matrices, the speedup of our implementation over the sequential algorithm increased to 3.19.
This document discusses patterns for parallel computing. It outlines key concepts like Amdahl's law and types of parallelism like data and task parallelism. Examples are provided of how major tech companies like Microsoft, Google, Amazon implement parallelism at different levels of their infrastructure and applications to scale efficiently. Design principles are discussed for converting sequential programs to parallel programs while maintaining performance.
Keras is a high-level neural networks API, written in Python and capable of running on top of either TensorFlow, CNTK or Theano.
We can easily build a model and train it using keras very easily with few lines of code.The steps to train the model is described in the presentation.
Use Keras if you need a deep learning library that:
-Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility).
-Supports both convolutional networks and recurrent networks, as well as combinations of the two.
-Runs seamlessly on CPU and GPU.
The theory behind parallel computing is covered here. For more theoretical knowledge: https://sites.google.com/view/vajira-thambawita/leaning-materials
This document discusses multivector and SIMD computers. It covers vector processing principles including vector instruction types like vector-vector, vector-scalar, and vector-memory instructions. It also discusses compound vector operations, vector loops and chaining. Finally, it discusses SIMD computer implementation models like distributed and shared memory, and SIMD instruction types.
Introduction to Segmentation in Computer vision ParrotAI
ย
Semantic segmentation is a dense prediction task that labels each pixel of an image with a class. It has applications in autonomous vehicles, medical imaging, and surgeries. Popular architectures for semantic segmentation include U-Net, which uses an encoder-decoder structure with skip connections, and Tiramisu, which uses dense blocks. The loss function commonly used is pixel-wise cross entropy loss, which examines predictions at each pixel.
Computer Architecture presentation covers topics like pipelining, VLIW architecture, and loop optimizations. Pipelining allows storing and executing instructions in an orderly process by dividing the instruction cycle into stages. VLIW was invented by Josh Fisher in the 1980s and breaks instructions into basic operations that can execute in parallel. Pipeline scheduling is used to run pipelines at regular intervals and has benefits for continuous integration like automating recurring tasks. Loop unrolling attempts to minimize loop overhead by manually expanding the loop body multiple times.
This document proposes extending algorithmic skeletons with event-driven programming to address the inversion of control problem in skeleton frameworks. It introduces event listeners that can be registered at event hooks within skeletons to access runtime information. This allows implementing non-functional concerns like logging and performance monitoring separately from the core parallel logic. The approach is implemented in the Skandium skeleton library, and examples are given of a logger and online performance monitor built using it. An analysis shows the overhead of processing events is negligible, at around 20 microseconds per event.
Implementing True Zero Cycle Branching in Scalar and Superscalar Pipelined Pr...IDES Editor
ย
In this paper, we have proposed a novel architectural
technique which can be used to boost performance of modern
day processors. It is especially useful in certain code constructs
like small loops and try-catch blocks. The technique is aimed
at improving performance by reducing the number of
instructions that need to enter the pipeline itself. We also
demonstrate its working in a scalar pipelined soft-core
processor developed by us. Lastly, we present how a superscalar
microprocessor can take advantage of this technique and
increase its performance.
This document discusses parallel processing concepts including:
1. Parallel computing involves simultaneously using multiple processing elements to solve problems faster than a single processor. Common parallel platforms include shared-memory and message-passing architectures.
2. Key considerations for parallel platforms include the control structure for specifying parallel tasks, communication models, and physical organization including interconnection networks.
3. Scalable design principles for parallel systems include avoiding single points of failure, pushing work away from the core, and designing for maintenance and automation. Common parallel architectures include N-wide superscalar, which can dispatch N instructions per cycle, and multi-core which places multiple cores on a single processor socket.
DESIGN AND ANALYSIS OF A 32-BIT PIPELINED MIPS RISC PROCESSORVLSICS Design
ย
Pipelining is a technique that exploits parallelism, among the instructions in a sequential instruction stream
to get increased throughput, and it lessens the total time to complete the work. . The major objective of this
architecture is to design a low power high performance structure which fulfils all the requirements of the
design. The critical factors like power, frequency, area, propagation delay are analysed using Spartan 3E
XC3E 1600e device with Xilinx tool.
Design and Analysis of A 32-bit Pipelined MIPS Risc ProcessorVLSICS Design
ย
The document describes the design and analysis of a 32-bit pipelined MIPS RISC processor. A 6-stage pipeline is implemented, consisting of instruction fetch, instruction decode, register read, memory access, execute, and write back stages. Various low power and high speed techniques are used, including power gating and deeper pipelining. The processor is implemented on a Spartan 3E FPGA and analyzed using Xilinx tools. Simulation results show the pipeline consumes low power of 0.129W and achieves a high frequency of 285.583MHz.
DESIGN AND ANALYSIS OF A 32-BIT PIPELINED MIPS RISC PROCESSORVLSICS Design
ย
The document describes the design and analysis of a 32-bit pipelined MIPS RISC processor. A 6-stage pipeline is implemented, consisting of instruction fetch, instruction decode, register read, memory access, execute, and write back stages. Various techniques are used to optimize critical performance factors like power, frequency, area, and propagation delay. Power gating is applied to minimize power consumption, and deeper pipelining is used to increase speed. Simulation results show the pipeline consumes very low power of 0.129W, has a path delay of 11.180ns, and achieves a high frequency of 285.583MHz.
This paper addresses the issue of accumulated computational and communication skew in time-stepped scientific applications running on cloud environments. It proposes a new approach called AsyTick that fully exploits parallelism among application ticks to resist skew accumulation. AsyTick uses a data-centric programming model and runtime system to allow decomposing computational parts of objects into asynchronous sub-processes. Experimental results show the proposed approach improves performance over state-of-the-art skew-resistant approaches by up to 2.53 times for time-stepped applications in the cloud.
A Survey of Machine Learning Methods Applied to Computer ...butest
ย
This document discusses various machine learning methods that have been applied to computer architecture problems. It begins by introducing k-means clustering and how it is used in SimPoint to reduce architecture simulation time. It then discusses how machine learning can be used for design space exploration in multi-core processors and for coordinated resource management on multiprocessors. Finally, it provides an example of using artificial neural networks to build performance models to inform resource allocation decisions.
This document discusses hardware and software parallelism in computer architecture. It defines hardware parallelism as the parallelism enabled by the machine architecture and hardware resources, such as the ability to issue multiple instructions per cycle. Software parallelism refers to the parallelism revealed by a program's control and data dependencies. There can be a mismatch between the hardware and software parallelism available. The document provides examples to illustrate this mismatch and the need for compiler support to better utilize the available hardware parallelism.
The document discusses computer architecture and organization. It provides questions and answers on topics such as:
- The definition of computer architecture and organization.
- The concept of layers in architectural design and their benefits.
- Differences between architecture and organization.
- Performance metrics and evaluating processor architecture.
- Examples of architectures like Pentium, servers, and the number of cycles for instructions on different processors.
Unit-4 discusses parallelism and techniques to exploit concurrency in computers. The goals of parallelism are to increase computational speed and throughput. There are different types of parallelism like instruction level parallelism, processor level parallelism using multiple processors, and pipelining to overlap instruction execution. Amdahl's law predicts the maximum speedup from parallel processing based on the sequential fraction of a program.
The document discusses parallelism and techniques to improve computer performance through parallel execution. It describes instruction level parallelism (ILP) where multiple instructions can be executed simultaneously through techniques like pipelining and superscalar processing. It also discusses processor level parallelism using multiple processors or processor cores to concurrently execute different tasks or threads.
Concurrent Matrix Multiplication on Multi-core ProcessorsCSCJournals
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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
Parallel processing involves performing multiple tasks simultaneously to increase computational speed. It can be achieved through pipelining, where instructions are overlapped in execution, or vector/array processors where the same operation is performed on multiple data elements at once. The main types are SIMD (single instruction multiple data) and MIMD (multiple instruction multiple data). Pipelining provides higher throughput by keeping the pipeline full but requires handling dependencies between instructions to avoid hazards slowing things down.
Parallelization of Graceful Labeling Using Open MPIJSRED
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This document summarizes research on parallelizing the graceful graph labeling problem using OpenMP on multi-core processors. It introduces the concepts of parallelization, multi-core architecture, and OpenMP. An algorithm is designed to parallelize graceful labeling by distributing graph vertices across processor cores. Execution time and speedup are measured for graphs of increasing size, showing improved speedup and reduced time with parallelization. Results show consistent performance gains as graph size increases due to better utilization of the multi-core architecture.
CS 301 Computer ArchitectureStudent # 1 EID 09Kingdom of .docxfaithxdunce63732
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This document summarizes the results of simulations run to analyze the performance of different processor configurations with varying levels of instruction-level parallelism. The key findings are:
1) For processors with significant memory latency, there is little performance difference between simple in-order and more complex out-of-order designs, as memory latency dominates execution time.
2) Supporting just two concurrently pending instructions provides most of the benefit of more complex out-of-order execution, while greatly reducing hardware complexity.
3) As the mismatch between processor and memory system performance increases, all designs see similar performance, regardless of the level of instruction-level parallelism exploited.
This document is a project report submitted by three students (Amit Kumar, Ankit Singh, and Sushant Bhadkamkar) for their Bachelor of Engineering degree in Computer Science. The report describes their work on a parallel computing cluster called Parallex. Parallex aims to create a high-performance computing system without requiring modifications to operating system kernels. It allows different operating systems and processor architectures to work together in parallel without using existing parallel libraries. The students implemented new distribution algorithms and parallel algorithms for Parallex to make administration and usage simple while maintaining efficiency.
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Integrating research and e learning in advance computer architecture
1.
2. ๏ Here we present methods in teaching advanced
computer architecture courses. These methods
include presenting fundamental computer
architecture issues using e-learning; employing
visual aids to teach fundamentals concepts like
Caches, pipelining and scheduling.
2
3. ๏ Advance Computer Architecture usually combines software and
hardware approaches that increase the performance of
microprocessor design.
๏ Main concepts in this courses includes measuring performance,
Instruction Set Design, Memory Hierarchy and Caches, Pipelining
and its Hazards, Instruction Level Parallelism, I/O storage, and
latest contemporary computer architecture issues.
๏ By using these concepts this course also presents the quantitative
approaches to measure the feasibility.
3
4. ๏ These approaches also measure the performance emphasizing on
the differences between hardware and software approaches.
๏ There are several books available on Computer Architecture
concepts.
๏ Hennessy and Pattersonโs are the one who gives a comprehensive
documentation on most of computer architecture topics.
4
5. Concepts for e-learning are
๏ Cache Associativity
๏ Superscalar microprocessors.
๏ Dynamic scheduling algorithms.
5
6. ๏ Definition
๏ It is the easy control of direct mapping cache and a fully associative
cache.
๏ Each cache location can have more than one pair of tag and data
item that resides at same location in cache memory.
๏ If one cache location is holding two pair of tag and data item that is
called two-way set associative cache.
6
7. ๏ A 2-way set associative cache
having 8 lines will have 4 sets
and each set has two lines.
๏ Figures show the set
associativity explain
๏ This approach presents the
cache to be split into number of
sets and each set has equal
number of lines.
7
8. Visual aid made the concepts easy to understand and we
can easily explained our point by adding visual aids and
graphics
๏ Pipelining and its hazards
๏ Superscalar design
๏ Instruction Level Parallelism
๏ Dynamic Scheduling.
8
9. ๏ Pipelining
๏ A Pipelining is a series of stages, where some work is done at each
stage in parallel.
๏ The stages are connected one to the next to form a pipe
instructions enter at one end, progress through the stages, and exit
at the other end.
๏ pipelining hazards
๏ Prevent the next instruction in the instruction stream from
being executing during its designated clock cycle.
๏ Hazards reduce the performance from the ideal speedup
gained by pipelining
9
10. ๏ DLX is simple pipelining
architecture for CPU.
๏ This is the seven clock cycle
that is required to execute the
instruction
K+(n-1) cycle
๏ The pipeline could be also
shown in terms of cycles,
meaning display the events at
each clock cycle
DLX pipeline Starting stage
DLX pipeline 2nd instruction
10
11. Pipelining hazards
๏ For pipeline hazards, the visual aid could show bubbles
inserted in the pipeline figure show bubbles and data
forwarded using arrows.
11
12. ๏ The concept of superscalars can also be explained with the
visual aids.
๏ This figure show a 2-way issues for a DLX superscalar
machine
๏ where one pipeline is assigned for integer and the other for
floating-point operations. Note that floating-point operation
takes 3 cycles to execute.
12
13. ๏ Definition
๏ Instruction level parallelism (ILP) is a measure of how
many of the instructions in a computer program can
be executed simultaneously.
๏ In Dynamic scheduling hardware determines which
instructions to execute,
๏ ILP and Dynamic Scheduling is made easy by using
visual aid.
13
14. Tomasulaโs algorithm
๏ It is a computer architecture hardware algorithm for
dynamic scheduling of instructions.
๏ It allows out-of-order execution and enables more efficient
use of multiple execution units.
๏ At cycle =0 five instructions
scheduled
๏ Student re-write each cycle
result.
๏ This idea involve the
student in the process of
learning and solving the
problem.
14
15. ๏ Advanced Computer Architecture is
rich with new topics that are in the
research stage.
๏ The student must be aware of these
topics before completing any advanced
computer architecture course.
15
16. ๏ Advanced Computer Architecture is rich with
advanced topics. The most advanced way of
learning is through visual aids and e-learning.
Future trends in teaching Computer Architecture
may lead to e-learning at a distance.
16