The document provides solutions to exercises related to computer architecture and systems. It includes solutions to 10 case studies and 18 exercises. The case studies cover topics like chip fabrication costs, power consumption in computer systems, optimizing cache performance, and highly parallel memory systems. The exercises reinforce concepts in areas such as performance modeling, vectorization, and memory hierarchy design.
This document discusses free space management techniques in operating systems. It explains the need to track free disk space and reuse it from deleted files. Various free space list implementations are described, including bit vector, linked list, grouping, and counting. Bit vector uses a bitmap to track free blocks, linked list links free blocks, grouping stores addresses of free blocks in blocks, and counting tracks free block runs with an address and count.
The document discusses the greedy method algorithmic approach. It provides an overview of greedy algorithms including that they make locally optimal choices at each step to find a global optimal solution. The document also provides examples of problems that can be solved using greedy methods like job sequencing, the knapsack problem, finding minimum spanning trees, and single source shortest paths. It summarizes control flow and applications of greedy algorithms.
This file contains the contents about dynamic programming, greedy approach, graph algorithm, spanning tree concepts, backtracking and branch and bound approach.
Unit – 4 discusses file system implementation topics including:
1. Free space management using techniques like linked lists, grouping, counting, and bit vectors to track unused blocks.
2. Methods to improve efficiency and performance such as disk caching, buffer caching, and read-ahead.
3. Recovery from crashes using consistency checking and file system backups to restore lost data.
4. Log structured file systems that record updates to metadata in a log to improve crash recovery.
5. The NFS protocol that allows accessing files over a network in a transparent manner through remote procedure calls.
Decision properties of reular languagesSOMNATHMORE2
This document discusses decision properties of regular languages. It defines regular languages as those that can be described by regular expressions and accepted by finite automata. It explains that decision properties are algorithms that take a formal language description and determine properties like emptiness, finiteness, membership in the language, and equivalence to another language. The key decision properties - emptiness, finiteness, membership, and equivalence - are then defined along with the algorithms to determine each. Examples are provided to illustrate the algorithms. Applications of decision properties in areas like data validation and parsing are also mentioned.
This document summarizes the n-queen problem, which involves placing N queens on an N x N chessboard so that no queen can attack any other. It describes the problem's inputs and tasks, provides examples of solutions for different board sizes, and outlines the backtracking algorithm commonly used to solve this problem. The backtracking approach guarantees a solution but can be slow, with complexity rising exponentially with problem size. It is a good benchmark for testing parallel computing systems due to its iterative nature.
This document describes the Hebbian learning rule, a single-layer neural network algorithm. The Hebbian rule updates weights between neurons based on their activation. Given an input, the output neuron's activation and the target output are used to update the weights according to the rule wi new = wi old + xiy. The document provides an example of using the Hebbian rule to train a network to perform the AND logic function over four training iterations. Over the iterations, the weights adjust until the network correctly classifies all four input patterns.
This document discusses free space management techniques in operating systems. It explains the need to track free disk space and reuse it from deleted files. Various free space list implementations are described, including bit vector, linked list, grouping, and counting. Bit vector uses a bitmap to track free blocks, linked list links free blocks, grouping stores addresses of free blocks in blocks, and counting tracks free block runs with an address and count.
The document discusses the greedy method algorithmic approach. It provides an overview of greedy algorithms including that they make locally optimal choices at each step to find a global optimal solution. The document also provides examples of problems that can be solved using greedy methods like job sequencing, the knapsack problem, finding minimum spanning trees, and single source shortest paths. It summarizes control flow and applications of greedy algorithms.
This file contains the contents about dynamic programming, greedy approach, graph algorithm, spanning tree concepts, backtracking and branch and bound approach.
Unit – 4 discusses file system implementation topics including:
1. Free space management using techniques like linked lists, grouping, counting, and bit vectors to track unused blocks.
2. Methods to improve efficiency and performance such as disk caching, buffer caching, and read-ahead.
3. Recovery from crashes using consistency checking and file system backups to restore lost data.
4. Log structured file systems that record updates to metadata in a log to improve crash recovery.
5. The NFS protocol that allows accessing files over a network in a transparent manner through remote procedure calls.
Decision properties of reular languagesSOMNATHMORE2
This document discusses decision properties of regular languages. It defines regular languages as those that can be described by regular expressions and accepted by finite automata. It explains that decision properties are algorithms that take a formal language description and determine properties like emptiness, finiteness, membership in the language, and equivalence to another language. The key decision properties - emptiness, finiteness, membership, and equivalence - are then defined along with the algorithms to determine each. Examples are provided to illustrate the algorithms. Applications of decision properties in areas like data validation and parsing are also mentioned.
This document summarizes the n-queen problem, which involves placing N queens on an N x N chessboard so that no queen can attack any other. It describes the problem's inputs and tasks, provides examples of solutions for different board sizes, and outlines the backtracking algorithm commonly used to solve this problem. The backtracking approach guarantees a solution but can be slow, with complexity rising exponentially with problem size. It is a good benchmark for testing parallel computing systems due to its iterative nature.
This document describes the Hebbian learning rule, a single-layer neural network algorithm. The Hebbian rule updates weights between neurons based on their activation. Given an input, the output neuron's activation and the target output are used to update the weights according to the rule wi new = wi old + xiy. The document provides an example of using the Hebbian rule to train a network to perform the AND logic function over four training iterations. Over the iterations, the weights adjust until the network correctly classifies all four input patterns.
Virtual Memory
• Copy-on-Write
• Page Replacement
• Allocation of Frames
• Thrashing
• Operating-System Examples
Background
Page Table When Some PagesAre Not in Main Memory
Steps in Handling a Page Fault
Given two integer arrays val[0...n-1] and wt[0...n-1] that represents values and weights associated with n items respectively. Find out the maximum value subset of val[] such that sum of the weights of this subset is smaller than or equal to knapsack capacity W. Here the BRANCH AND BOUND ALGORITHM is discussed .
The document discusses the knapsack problem and greedy algorithms. It defines the knapsack problem as an optimization problem where given constraints and an objective function, the goal is to find the feasible solution that maximizes or minimizes the objective. It describes the knapsack problem has having two versions: 0-1 where items are indivisible, and fractional where items can be divided. The fractional knapsack problem can be solved using a greedy approach by sorting items by value to weight ratio and filling the knapsack accordingly until full.
SCSJ3553 - Artificial Intelligence Final Exam paper - UTMAbdul Khaliq
This document contains a 14-page AI exam with multiple choice, short answer, and structured questions. It tests knowledge of search techniques, knowledge representation, production systems, and other AI concepts. The exam is divided into sections on true/false questions, short explanations, and longer structured questions involving search algorithms, knowledge representation diagrams, and production systems examples.
Alpha-beta pruning is an optimization technique for minimax algorithms that reduces computation time by pruning branches that are provably non-optimal. It works by maintaining alpha and beta values that represent the best values found for the maximizing and minimizing players. Branches can be pruned when alpha is greater than or equal to beta, without affecting the optimal outcome. Alpha-beta pruning allows searching much deeper levels of the game tree in the same amount of time as minimax.
https://github.com/ashim888/dataStructureAndAlgorithm
Stack
Concept and Definition
• Primitive Operations
• Stack as an ADT
• Implementing PUSH and POP operation
• Testing for overflow and underflow conditions
Recursion
• Concept and Definition
• Implementation of:
¬ Multiplication of Natural Numbers
¬ Factorial
¬ Fibonacci Sequences
The Tower of Hanoi
Greedy algorithms work by making locally optimal choices at each step to arrive at a global optimal solution. They require that the problem exhibits the greedy choice property and optimal substructure. Examples that can be solved with greedy algorithms include fractional knapsack problem, minimum spanning tree, and activity selection. The fractional knapsack problem is solved greedily by sorting items by value/weight ratio and filling the knapsack completely. The 0/1 knapsack problem differs in that items are indivisible.
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
This PPT is all about the Tree basic on fundamentals of B and B+ Tree with it's Various (Search,Insert and Delete) Operations performed on it and their Examples...
Dynamic programming is an algorithm design paradigm that can be applied to problems exhibiting optimal substructure and overlapping subproblems. It works by breaking down a problem into subproblems and storing the results of already solved subproblems, rather than recomputing them multiple times. This allows for an efficient bottom-up approach. Examples where dynamic programming can be applied include the matrix chain multiplication problem, the 0-1 knapsack problem, and finding the longest common subsequence between two strings.
B-Trees are tree data structures used to store data on disk storage. They allow for efficient retrieval of data compared to binary trees when using disk storage due to reduced height. B-Trees group data into nodes that can have multiple children, reducing the height needed compared to binary trees. Keys are inserted by adding to leaf nodes or splitting nodes and promoting middle keys. Deletion involves removing from leaf nodes, borrowing/promoting keys, or joining nodes.
This document discusses various problems that can be solved using backtracking, including graph coloring, the Hamiltonian cycle problem, the subset sum problem, the n-queen problem, and map coloring. It provides examples of how backtracking works by constructing partial solutions and evaluating them to find valid solutions or determine dead ends. Key terms like state-space trees and promising vs non-promising states are introduced. Specific examples are given for problems like placing 4 queens on a chessboard and coloring a map of Australia.
Binary search is a fast search algorithm that works on sorted data by comparing the middle element of the collection to the target value. It divides the search space in half at each step to quickly locate an element. The algorithm gets the middle element, compares it to the target, and either searches the left or right half recursively depending on if the target is less than or greater than the middle element. An example demonstrates finding the value 23 in a sorted array using this divide and conquer approach.
This document provides information about genetic algorithms including:
1. Definitions of genetic algorithms from Grefenstette and Goldberg that describe genetic algorithms as search algorithms based on biological evolution and natural selection.
2. An overview of genetic algorithms including the basic concepts of populations, chromosomes, genes, fitness functions, selection, crossover, and mutation.
3. Examples of genetic representations like binary encoding and permutation encoding.
4. Descriptions of genetic operators like selection, crossover, and mutation that maintain genetic diversity between generations.
The document discusses the 0-1 knapsack problem and how it can be solved using dynamic programming. It first defines the 0-1 knapsack problem and provides an example. It then explains how a brute force solution would work in exponential time. Next, it describes how to define the problem as subproblems and derive a recursive formula to solve the subproblems in a bottom-up manner using dynamic programming. This builds up the solutions in a table and solves the problem in polynomial time. Finally, it walks through an example applying the dynamic programming algorithm to a sample problem instance.
P, NP, NP-Complete, and NP-Hard
Reductionism in Algorithms
NP-Completeness and Cooks Theorem
NP-Complete and NP-Hard Problems
Travelling Salesman Problem (TSP)
Travelling Salesman Problem (TSP) - Approximation Algorithms
PRIMES is in P - (A hope for NP problems in P)
Millennium Problems
Conclusions
Memory management is the act of managing computer memory. The essential requirement of memory management is to provide ways to dynamically allocate portions of memory to programs at their request, and free it for reuse when no longer needed. This is critical to any advanced computer system where more than a single process might be underway at any time
Trace scheduling involves forming larger regions called traces from multiple basic blocks to improve instruction scheduling. Traces are constructed by following the most likely path through conditional branches. Optimizations like speculative code motion can then move instructions across branches within the trace. Compensation code may be needed to fix program semantics when instructions are speculatively moved or code is duplicated. The goal is to avoid branches and exploit a larger scheduling region for more optimization opportunities than single basic blocks.
- Directory structures organize files in a storage system and contain metadata about each file's name, location, size, and type. They allow operations like creating, searching, deleting, listing, and renaming files.
- Early systems used single-level directories with one list of all files, but this does not allow multiple files with the same name or grouping of files.
- Modern systems commonly use tree-structured directories that allow nesting files into subdirectories, making searching more efficient and allowing grouping of similar files. Directories can also be connected in acyclic graphs to enable sharing of files between directories through links.
The document discusses dense linear algebra solvers and algorithms. It provides an overview of existing software for dense linear algebra including LINPACK, EISPACK, LAPACK, ScaLAPACK, PLASMA, and MAGMA. It then discusses challenges with dense linear algebra on modern hardware including distributed memory, heterogeneity, and the high cost of communication. It introduces tile algorithms as an approach to address these challenges compared to traditional LAPACK algorithms.
Amazon EC2 provides a broad selection of instance types to accommodate a diverse mix of workloads. In this session, we provide an overview of the Amazon EC2 instance platform, key platform features, and the concept of instance generations. We dive into the current generation design choices of the different instance families, including the General Purpose, Compute Optimized, Storage Optimized, Memory Optimized, and GPU instance families. We also detail best practices and share performance tips for getting the most out of your Amazon EC2 instances.
Virtual Memory
• Copy-on-Write
• Page Replacement
• Allocation of Frames
• Thrashing
• Operating-System Examples
Background
Page Table When Some PagesAre Not in Main Memory
Steps in Handling a Page Fault
Given two integer arrays val[0...n-1] and wt[0...n-1] that represents values and weights associated with n items respectively. Find out the maximum value subset of val[] such that sum of the weights of this subset is smaller than or equal to knapsack capacity W. Here the BRANCH AND BOUND ALGORITHM is discussed .
The document discusses the knapsack problem and greedy algorithms. It defines the knapsack problem as an optimization problem where given constraints and an objective function, the goal is to find the feasible solution that maximizes or minimizes the objective. It describes the knapsack problem has having two versions: 0-1 where items are indivisible, and fractional where items can be divided. The fractional knapsack problem can be solved using a greedy approach by sorting items by value to weight ratio and filling the knapsack accordingly until full.
SCSJ3553 - Artificial Intelligence Final Exam paper - UTMAbdul Khaliq
This document contains a 14-page AI exam with multiple choice, short answer, and structured questions. It tests knowledge of search techniques, knowledge representation, production systems, and other AI concepts. The exam is divided into sections on true/false questions, short explanations, and longer structured questions involving search algorithms, knowledge representation diagrams, and production systems examples.
Alpha-beta pruning is an optimization technique for minimax algorithms that reduces computation time by pruning branches that are provably non-optimal. It works by maintaining alpha and beta values that represent the best values found for the maximizing and minimizing players. Branches can be pruned when alpha is greater than or equal to beta, without affecting the optimal outcome. Alpha-beta pruning allows searching much deeper levels of the game tree in the same amount of time as minimax.
https://github.com/ashim888/dataStructureAndAlgorithm
Stack
Concept and Definition
• Primitive Operations
• Stack as an ADT
• Implementing PUSH and POP operation
• Testing for overflow and underflow conditions
Recursion
• Concept and Definition
• Implementation of:
¬ Multiplication of Natural Numbers
¬ Factorial
¬ Fibonacci Sequences
The Tower of Hanoi
Greedy algorithms work by making locally optimal choices at each step to arrive at a global optimal solution. They require that the problem exhibits the greedy choice property and optimal substructure. Examples that can be solved with greedy algorithms include fractional knapsack problem, minimum spanning tree, and activity selection. The fractional knapsack problem is solved greedily by sorting items by value/weight ratio and filling the knapsack completely. The 0/1 knapsack problem differs in that items are indivisible.
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
This PPT is all about the Tree basic on fundamentals of B and B+ Tree with it's Various (Search,Insert and Delete) Operations performed on it and their Examples...
Dynamic programming is an algorithm design paradigm that can be applied to problems exhibiting optimal substructure and overlapping subproblems. It works by breaking down a problem into subproblems and storing the results of already solved subproblems, rather than recomputing them multiple times. This allows for an efficient bottom-up approach. Examples where dynamic programming can be applied include the matrix chain multiplication problem, the 0-1 knapsack problem, and finding the longest common subsequence between two strings.
B-Trees are tree data structures used to store data on disk storage. They allow for efficient retrieval of data compared to binary trees when using disk storage due to reduced height. B-Trees group data into nodes that can have multiple children, reducing the height needed compared to binary trees. Keys are inserted by adding to leaf nodes or splitting nodes and promoting middle keys. Deletion involves removing from leaf nodes, borrowing/promoting keys, or joining nodes.
This document discusses various problems that can be solved using backtracking, including graph coloring, the Hamiltonian cycle problem, the subset sum problem, the n-queen problem, and map coloring. It provides examples of how backtracking works by constructing partial solutions and evaluating them to find valid solutions or determine dead ends. Key terms like state-space trees and promising vs non-promising states are introduced. Specific examples are given for problems like placing 4 queens on a chessboard and coloring a map of Australia.
Binary search is a fast search algorithm that works on sorted data by comparing the middle element of the collection to the target value. It divides the search space in half at each step to quickly locate an element. The algorithm gets the middle element, compares it to the target, and either searches the left or right half recursively depending on if the target is less than or greater than the middle element. An example demonstrates finding the value 23 in a sorted array using this divide and conquer approach.
This document provides information about genetic algorithms including:
1. Definitions of genetic algorithms from Grefenstette and Goldberg that describe genetic algorithms as search algorithms based on biological evolution and natural selection.
2. An overview of genetic algorithms including the basic concepts of populations, chromosomes, genes, fitness functions, selection, crossover, and mutation.
3. Examples of genetic representations like binary encoding and permutation encoding.
4. Descriptions of genetic operators like selection, crossover, and mutation that maintain genetic diversity between generations.
The document discusses the 0-1 knapsack problem and how it can be solved using dynamic programming. It first defines the 0-1 knapsack problem and provides an example. It then explains how a brute force solution would work in exponential time. Next, it describes how to define the problem as subproblems and derive a recursive formula to solve the subproblems in a bottom-up manner using dynamic programming. This builds up the solutions in a table and solves the problem in polynomial time. Finally, it walks through an example applying the dynamic programming algorithm to a sample problem instance.
P, NP, NP-Complete, and NP-Hard
Reductionism in Algorithms
NP-Completeness and Cooks Theorem
NP-Complete and NP-Hard Problems
Travelling Salesman Problem (TSP)
Travelling Salesman Problem (TSP) - Approximation Algorithms
PRIMES is in P - (A hope for NP problems in P)
Millennium Problems
Conclusions
Memory management is the act of managing computer memory. The essential requirement of memory management is to provide ways to dynamically allocate portions of memory to programs at their request, and free it for reuse when no longer needed. This is critical to any advanced computer system where more than a single process might be underway at any time
Trace scheduling involves forming larger regions called traces from multiple basic blocks to improve instruction scheduling. Traces are constructed by following the most likely path through conditional branches. Optimizations like speculative code motion can then move instructions across branches within the trace. Compensation code may be needed to fix program semantics when instructions are speculatively moved or code is duplicated. The goal is to avoid branches and exploit a larger scheduling region for more optimization opportunities than single basic blocks.
- Directory structures organize files in a storage system and contain metadata about each file's name, location, size, and type. They allow operations like creating, searching, deleting, listing, and renaming files.
- Early systems used single-level directories with one list of all files, but this does not allow multiple files with the same name or grouping of files.
- Modern systems commonly use tree-structured directories that allow nesting files into subdirectories, making searching more efficient and allowing grouping of similar files. Directories can also be connected in acyclic graphs to enable sharing of files between directories through links.
The document discusses dense linear algebra solvers and algorithms. It provides an overview of existing software for dense linear algebra including LINPACK, EISPACK, LAPACK, ScaLAPACK, PLASMA, and MAGMA. It then discusses challenges with dense linear algebra on modern hardware including distributed memory, heterogeneity, and the high cost of communication. It introduces tile algorithms as an approach to address these challenges compared to traditional LAPACK algorithms.
Amazon EC2 provides a broad selection of instance types to accommodate a diverse mix of workloads. In this session, we provide an overview of the Amazon EC2 instance platform, key platform features, and the concept of instance generations. We dive into the current generation design choices of the different instance families, including the General Purpose, Compute Optimized, Storage Optimized, Memory Optimized, and GPU instance families. We also detail best practices and share performance tips for getting the most out of your Amazon EC2 instances.
Chapter 8 1 Digital Design and Computer Architecture, 2n.docxchristinemaritza
Chapter 8 <1>
Digital Design and Computer Architecture, 2nd Edition
Chapter 8
David Money Harris and Sarah L. Harris
Chapter 8 <2>
Chapter 8 :: Topics
• Introduction
• Memory System Performance
Analysis
• Caches
• Virtual Memory
• Memory-Mapped I/O
• Summary
Chapter 8 <3>
Processor Memory
Address
MemWrite
WriteData
ReadData
WE
CLKCLK
• Computer performance depends on:
– Processor performance
– Memory system performance
Memory Interface
Introduction
Chapter 8 <4>
In prior chapters, assumed access memory in 1 clock
cycle – but hasn’t been true since the 1980’s
Processor-Memory Gap
Chapter 8 <5>
• Make memory system appear as fast as
processor
• Use hierarchy of memories
• Ideal memory:
– Fast
– Cheap (inexpensive)
– Large (capacity)
But can only choose two!
Memory System Challenge
Chapter 8 <6>
Memory Hierarchy
Technology Price / GB
Access
Time (ns)
Bandwidth
(GB/s)
Cache
Main Memory
Virtual Memory
Capacity
S
p
e
e
d
SRAM $10,000 1
DRAM $10 10 - 50
SSD $1 100,000
25+
10
0.5
0.1HDD $0.1 10,000,000
Chapter 8 <7>
Exploit locality to make memory accesses fast
• Temporal Locality:
– Locality in time
– If data used recently, likely to use it again soon
– How to exploit: keep recently accessed data in higher
levels of memory hierarchy
• Spatial Locality:
– Locality in space
– If data used recently, likely to use nearby data soon
– How to exploit: when access data, bring nearby data
into higher levels of memory hierarchy too
Locality
Chapter 8 <8>
• Hit: data found in that level of memory hierarchy
• Miss: data not found (must go to next level)
Hit Rate = # hits / # memory accesses
= 1 – Miss Rate
Miss Rate = # misses / # memory accesses
= 1 – Hit Rate
• Average memory access time (AMAT): average time
for processor to access data
AMAT = tcache + MRcache[tMM + MRMM(tVM)]
Memory Performance
Chapter 8 <9>
• A program has 2,000 loads and stores
• 1,250 of these data values in cache
• Rest supplied by other levels of memory
hierarchy
• What are the hit and miss rates for the cache?
Memory Performance Example 1
Chapter 8 <10>
• A program has 2,000 loads and stores
• 1,250 of these data values in cache
• Rest supplied by other levels of memory
hierarchy
• What are the hit and miss rates for the cache?
Hit Rate = 1250/2000 = 0.625
Miss Rate = 750/2000 = 0.375 = 1 – Hit Rate
Memory Performance Example 1
Chapter 8 <11>
• Suppose processor has 2 levels of hierarchy:
cache and main memory
• tcache = 1 cycle, tMM = 100 cycles
• What is the AMAT of the program from
Example 1?
Memory Performance Example 2
Chapter 8 <12>
• Suppose processor has 2 levels of hierarchy:
cache and main memory
• tcache = 1 cycle, tMM = 100 cycles
• What is the AMAT of the program from
Example 1?
AMAT = tcache + MRcache(tMM)
= [1 + 0.375(100)] cycles
= 38.5 cycles
Memory Performance Example 2
Chapter 8 <13>
• Amdahl’s Law: the
effort spent incr ...
SRV402 Deep Dive on Amazon EC2 Instances, Featuring Performance Optimization ...Amazon Web Services
Amazon EC2 provides a broad selection of instance types to accommodate a diverse mix of workloads. In this session, we provide an overview of the Amazon EC2 instance platform, key platform features, and the concept of instance generations. We dive into the current generation design choices of the different instance families, including the General Purpose, Compute Optimized, Storage Optimized, Memory Optimized, and Accelerated Computing (GPU and FPGA) instance families. We also detail best practices and share performance tips for getting the most out of your Amazon EC2 instances.
SRV402 Deep Dive on Amazon EC2 Instances, Featuring Performance Optimization ...Amazon Web Services
Amazon EC2 provides a broad selection of instance types to accommodate a diverse mix of workloads. In this session, we provide an overview of the Amazon EC2 instance platform, key platform features, and the concept of instance generations. We dive into the current generation design choices of the different instance families, including the General Purpose, Compute Optimized, Storage Optimized, Memory Optimized, and Accelerated Computing (GPU and FPGA) instance families. We also detail best practices and share performance tips for getting the most out of your Amazon EC2 instances.
This document contains problems and solutions related to pipelining and superscalar techniques in computer architecture. It discusses speedup factors, efficiency, throughput, and latency for a pipelined processor. It also analyzes the DEC Alpha architecture in terms of scalability and addresses a multiprocessor implementation. Several problems are solved related to reservation tables, collision vectors, state transition diagrams, and determining minimum average latency for pipeline scheduling.
Advanced Comuter Architecture Ch6 Problem SolutionsJoe Christensen
This document contains problems and solutions related to pipelining and superscalar techniques in computer architecture. It discusses speedup factors, efficiency, throughput, and latency for a pipelined processor. It also analyzes the DEC Alpha architecture in terms of scalability and addresses a multiprocessor implementation. Several problems are solved related to reservation tables, collision vectors, state transition diagrams, and determining minimum average latency for pipeline scheduling.
This document discusses the Meltdown and Spectre vulnerabilities that were discovered in modern CPUs. Meltdown allows reading kernel memory from user space by exploiting out-of-order execution and speculative execution. Spectre attacks exploit speculative execution to access sensitive information through side channels. The document explains speculative execution, how Meltdown works by reading kernel memory speculatively, and the two variants of Spectre attacks - bound check bypass and branch target injection. Mitigations like KPTI and inserting speculative execution blocking instructions are discussed. The vulnerabilities are considered some of the greatest in computer history due to their fundamental exploitation of CPU designs.
This document discusses the Meltdown and Spectre vulnerabilities that were discovered in modern CPUs. Meltdown allows reading kernel memory from user space by exploiting out-of-order execution and speculative execution. Spectre attacks exploit speculative execution to access sensitive information through side channels. The document explains speculative execution, how Meltdown works by inducing mispredictions and reading memory access times, and the two variants of Spectre that exploit conditional branches and indirect branches. Mitigations like KPTI and inserting blocking instructions are discussed along with the performance trade-offs of addressing these vulnerabilities.
Mcs 012 computer organisation and assemly language programming- ignou assignm...Dr. Loganathan R
The document discusses various computer architecture concepts including:
1. A hypothetical new machine is described with 64 64-bit general purpose registers, 2GB of 32-bit memory, and instructions that are one or two memory words. Four addressing modes are needed: direct, index, base register, and stack to access variables and arrays.
2. Terms related to magnetic disk access are defined, including tracks, sectors, seek time, rotational latency, transfer time, and access time. Calculations are shown to find the average access time of 13.04ms for a 2048 byte sector disk rotating at 3000 RPM with a 64MB/s transfer rate.
3. Input/output techniques like programmed I/O,
Alex Smola, Professor in the Machine Learning Department, Carnegie Mellon Uni...MLconf
Fast, Cheap and Deep – Scaling Machine Learning: Distributed high throughput machine learning is both a challenge and a key enabling technology. Using a Parameter Server template we are able to distribute algorithms efficiently over multiple GPUs and in the cloud. This allows us to design very fast recommender systems, factorization machines, classifiers, and deep networks. This degree of scalability allows us to tackle computationally expensive problems efficiently, yielding excellent results e.g. in visual question answering.
Optimizing the Graphics Pipeline with Compute, GDC 2016Graham Wihlidal
With further advancement in the current console cycle, new tricks are being learned to squeeze the maximum performance out of the hardware. This talk will present how the compute power of the console and PC GPUs can be used to improve the triangle throughput beyond the limits of the fixed function hardware. The discussed method shows a way to perform efficient "just-in-time" optimization of geometry, and opens the way for per-primitive filtering kernels and procedural geometry processing.
Takeaway:
Attendees will learn how to preprocess geometry on-the-fly per frame to improve rendering performance and efficiency.
Intended Audience:
This presentation is targeting seasoned graphics developers. Experience with DirectX 12 and GCN is recommended, but not required.
Optimizing Parallel Reduction in CUDA : NOTESSubhajit Sahu
Highlighted notes on Optimizing Parallel Reduction in CUDA
While doing research work under Prof. Dip Banerjee, Prof. Kishore Kothapalli.
Interesting optimizations, i should try these soon as PageRank is basically lots of sums.
1) The document contains solutions to chapter 8 homework problems involving page tables, page replacement policies, working set size, and memory management with both paging and segmentation.
2) Key concepts covered include virtual to physical address translation using page tables, page replacement algorithms like FIFO and LRU, calculating memory access rates, and addressing with multiple levels of page tables and segmentation.
3) Memory management techniques like page tables, page replacement, and multi-level addressing schemes are analyzed to solve problems involving virtual memory configuration, address translation, and modeling memory access patterns.
SRV402 Deep Dive on Amazon EC2 Instances, Featuring Performance Optimization ...Amazon Web Services
Amazon EC2 provides a broad selection of instance types to accommodate a diverse mix of workloads. In this session, we provide an overview of the Amazon EC2 instance platform, key platform features, and the concept of instance generations. We dive into the current generation design choices of the different instance families, including the General Purpose, Compute Optimized, Storage Optimized, Memory Optimized, and Accelerated Computing (GPU and FPGA) instance families. We also detail best practices and share performance tips for getting the most out of your Amazon EC2 instances.
The document discusses exploiting a vulnerability in Cisco ASA firewall devices. It begins with background on the target device and vulnerability, then outlines steps for getting access to the firmware, debugging the target, and identifying the vulnerability through static and dynamic analysis. The document then covers techniques for triggering the vulnerability and developing a controlled exploit to achieve remote code execution without user interaction.
This document provides an introduction to linear programming. It defines linear programming as an optimization problem that involves maximizing or minimizing a linear objective function subject to linear constraints. Various terminology used in linear programming like decision variables, objective function, and constraints are explained. Several examples of linear programming problems from areas like production planning, scheduling, and resource allocation are presented and formulated mathematically. Graphical and algebraic solution methods for linear programming problems are discussed. The document also notes that integer programming problems cannot be solved using the same techniques as linear programs due to the discrete nature of the variables. Additional linear programming examples and problems from an operations research textbook are listed for further practice.
CS6401 Operating systems - Solved Examplesramyaranjith
The document summarizes information about scheduling algorithms, memory management techniques, and disk scheduling algorithms. It provides examples and calculations to illustrate different concepts in operating systems like page replacement policies, scheduling processes with different algorithms, banker's algorithm, memory allocation strategies, demand paging, and disk scheduling. The document contains questions related to these topics and provides step-by-step solutions and explanations.
This document discusses techniques for reducing cache misses and improving memory performance. It introduces the concepts of compulsory, capacity and conflict misses. Methods covered for reducing misses include increasing block size, associativity, using victim caches, pseudo-associativity, hardware/software prefetching, and compiler optimizations like merging arrays, loop interchange, fusion and blocking. Both hardware and software prefetching are described as well as the tradeoffs between binding and non-binding prefetching.
Similar to Computer architecture, a quantitative approach (solution for 5th edition) (20)
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
-------------------------------------------------------------------------------
Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
-------------------------------------------------------------------------------
For more information about PECB:
Website: https://pecb.com/
LinkedIn: https://www.linkedin.com/company/pecb/
Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.