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18.337J Parallel Computing (MIT) 18.337J Parallel Computing (MIT)

Description

This is an advanced interdisciplinary introduction to applied parallel computing on modern supercomputers. It has a hands-on emphasis on understanding the realities and myths of what is possible on the world's fastest machines. We will make prominent use of the Julia Language, a free, open-source, high-performance dynamic programming language for technical computing. This is an advanced interdisciplinary introduction to applied parallel computing on modern supercomputers. It has a hands-on emphasis on understanding the realities and myths of what is possible on the world's fastest machines. We will make prominent use of the Julia Language, a free, open-source, high-performance dynamic programming language for technical computing.Subjects

cloud computing | cloud computing | dense linear algebra | dense linear algebra | sparse linear algebra | sparse linear algebra | N-body problems | N-body problems | multigrid | multigrid | fast-multipole | fast-multipole | wavelets | wavelets | Fourier transforms | Fourier transforms | partitioning | partitioning | mesh generation | mesh generation | applications oriented architecture | applications oriented architecture | parallel programming paradigms | parallel programming paradigms | MPI | MPI | data parallel systems | data parallel systems | Star-P | Star-P | parallel Python | parallel Python | parallel Matlab | parallel Matlab | graphics processors | graphics processors | virtualization | virtualization | caches | caches | vector processors | vector processors | VHLLs | VHLLs | Very High Level Languages | Very High Level Languages | Julia programming language | Julia programming language | distributed parallel execution | distributed parallel executionLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata6.856J Randomized Algorithms (MIT) 6.856J Randomized Algorithms (MIT)

Description

This course examines how randomization can be used to make algorithms simpler and more efficient via random sampling, random selection of witnesses, symmetry breaking, and Markov chains. Topics covered include: randomized computation; data structures (hash tables, skip lists); graph algorithms (minimum spanning trees, shortest paths, minimum cuts); geometric algorithms (convex hulls, linear programming in fixed or arbitrary dimension); approximate counting; parallel algorithms; online algorithms; derandomization techniques; and tools for probabilistic analysis of algorithms. This course examines how randomization can be used to make algorithms simpler and more efficient via random sampling, random selection of witnesses, symmetry breaking, and Markov chains. Topics covered include: randomized computation; data structures (hash tables, skip lists); graph algorithms (minimum spanning trees, shortest paths, minimum cuts); geometric algorithms (convex hulls, linear programming in fixed or arbitrary dimension); approximate counting; parallel algorithms; online algorithms; derandomization techniques; and tools for probabilistic analysis of algorithms.Subjects

Randomized Algorithms | Randomized Algorithms | algorithms | algorithms | efficient in time and space | efficient in time and space | randomization | randomization | computational problems | computational problems | data structures | data structures | graph algorithms | graph algorithms | optimization | optimization | geometry | geometry | Markov chains | Markov chains | sampling | sampling | estimation | estimation | geometric algorithms | geometric algorithms | parallel and distributed algorithms | parallel and distributed algorithms | parallel and ditributed algorithm | parallel and ditributed algorithm | parallel and distributed algorithm | parallel and distributed algorithm | random sampling | random sampling | random selection of witnesses | random selection of witnesses | symmetry breaking | symmetry breaking | randomized computational models | randomized computational models | hash tables | hash tables | skip lists | skip lists | minimum spanning trees | minimum spanning trees | shortest paths | shortest paths | minimum cuts | minimum cuts | convex hulls | convex hulls | linear programming | linear programming | fixed dimension | fixed dimension | arbitrary dimension | arbitrary dimension | approximate counting | approximate counting | parallel algorithms | parallel algorithms | online algorithms | online algorithms | derandomization techniques | derandomization techniques | probabilistic analysis | probabilistic analysis | computational number theory | computational number theory | simplicity | simplicity | speed | speed | design | design | basic probability theory | basic probability theory | application | application | randomized complexity classes | randomized complexity classes | game-theoretic techniques | game-theoretic techniques | Chebyshev | Chebyshev | moment inequalities | moment inequalities | limited independence | limited independence | coupon collection | coupon collection | occupancy problems | occupancy problems | tail inequalities | tail inequalities | Chernoff bound | Chernoff bound | conditional expectation | conditional expectation | probabilistic method | probabilistic method | random walks | random walks | algebraic techniques | algebraic techniques | probability amplification | probability amplification | sorting | sorting | searching | searching | combinatorial optimization | combinatorial optimization | approximation | approximation | counting problems | counting problems | distributed algorithms | distributed algorithms | 6.856 | 6.856 | 18.416 | 18.416License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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The topics covered in this course include: Languages and compilers to exploit multithreaded parallelism Implicit parallel programming using functional languages and their extensions Higher-order functions, non-strictness, and polymorphism Explicit parallel programming and nondeterminism The lambda calculus and its variants Term rewriting and operational semantics Compiling multithreaded code for symmetric multiprocessors and clusters Static analysis and compiler optimizations This course is worth 4 Engineering Design Points. The topics covered in this course include: Languages and compilers to exploit multithreaded parallelism Implicit parallel programming using functional languages and their extensions Higher-order functions, non-strictness, and polymorphism Explicit parallel programming and nondeterminism The lambda calculus and its variants Term rewriting and operational semantics Compiling multithreaded code for symmetric multiprocessors and clusters Static analysis and compiler optimizations This course is worth 4 Engineering Design Points.Subjects

languages | languages | compilers | compilers | multithreaded parallelism | multithreaded parallelism | implicit parallel programming | implicit parallel programming | higher order functions | higher order functions | non-strictness | non-strictness | polymorphism | polymorphism | explicit parallel programming | explicit parallel programming | nondeterminism | nondeterminism | lambda calculus | lambda calculus | term rewriting | term rewriting | symmetric multiprocessors | symmetric multiprocessors | clusters | clusters | static analysis | static analysis | compiler optimizations | compiler optimizationsLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata18.337J Parallel Computing (MIT)

Description

This is an advanced interdisciplinary introduction to applied parallel computing on modern supercomputers. It has a hands-on emphasis on understanding the realities and myths of what is possible on the world's fastest machines. We will make prominent use of the Julia Language, a free, open-source, high-performance dynamic programming language for technical computing.Subjects

cloud computing | dense linear algebra | sparse linear algebra | N-body problems | multigrid | fast-multipole | wavelets | Fourier transforms | partitioning | mesh generation | applications oriented architecture | parallel programming paradigms | MPI | data parallel systems | Star-P | parallel Python | parallel Matlab | graphics processors | virtualization | caches | vector processors | VHLLs | Very High Level Languages | Julia programming language | distributed parallel executionLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata6.856J Randomized Algorithms (MIT)

Description

This course examines how randomization can be used to make algorithms simpler and more efficient via random sampling, random selection of witnesses, symmetry breaking, and Markov chains. Topics covered include: randomized computation; data structures (hash tables, skip lists); graph algorithms (minimum spanning trees, shortest paths, minimum cuts); geometric algorithms (convex hulls, linear programming in fixed or arbitrary dimension); approximate counting; parallel algorithms; online algorithms; derandomization techniques; and tools for probabilistic analysis of algorithms.Subjects

Randomized Algorithms | algorithms | efficient in time and space | randomization | computational problems | data structures | graph algorithms | optimization | geometry | Markov chains | sampling | estimation | geometric algorithms | parallel and distributed algorithms | parallel and ditributed algorithm | parallel and distributed algorithm | random sampling | random selection of witnesses | symmetry breaking | randomized computational models | hash tables | skip lists | minimum spanning trees | shortest paths | minimum cuts | convex hulls | linear programming | fixed dimension | arbitrary dimension | approximate counting | parallel algorithms | online algorithms | derandomization techniques | probabilistic analysis | computational number theory | simplicity | speed | design | basic probability theory | application | randomized complexity classes | game-theoretic techniques | Chebyshev | moment inequalities | limited independence | coupon collection | occupancy problems | tail inequalities | Chernoff bound | conditional expectation | probabilistic method | random walks | algebraic techniques | probability amplification | sorting | searching | combinatorial optimization | approximation | counting problems | distributed algorithms | 6.856 | 18.416License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata6.854J Advanced Algorithms (MIT) 6.854J Advanced Algorithms (MIT)

Description

This course is a first-year graduate course in algorithms. Emphasis is placed on fundamental algorithms and advanced methods of algorithmic design, analysis, and implementation. Techniques to be covered include amortization, randomization, fingerprinting, word-level parallelism, bit scaling, dynamic programming, network flow, linear programming, fixed-parameter algorithms, and approximation algorithms. Domains include string algorithms, network optimization, parallel algorithms, computational geometry, online algorithms, external memory, cache, and streaming algorithms, and data structures. This course is a first-year graduate course in algorithms. Emphasis is placed on fundamental algorithms and advanced methods of algorithmic design, analysis, and implementation. Techniques to be covered include amortization, randomization, fingerprinting, word-level parallelism, bit scaling, dynamic programming, network flow, linear programming, fixed-parameter algorithms, and approximation algorithms. Domains include string algorithms, network optimization, parallel algorithms, computational geometry, online algorithms, external memory, cache, and streaming algorithms, and data structures.Subjects

amortization | amortization | randomization | randomization | fingerprinting | fingerprinting | word-level parallelism | word-level parallelism | bit scaling | bit scaling | dynamic programming | dynamic programming | network flow | network flow | linear programming | linear programming | fixed-parameter algorithms | fixed-parameter algorithms | approximation algorithms | approximation algorithms | string algorithms | string algorithms | network optimization | network optimization | parallel algorithms | parallel algorithms | computational geometry | computational geometry | online algorithms | online algorithms | external memory | external memory | external cache | external cache | external streaming | external streaming | data structures | data structuresLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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Modern computing platforms provide unprecedented amounts of raw computational power. But significant complexity comes along with this power, to the point that making useful computations exploit even a fraction of the potential of the computing platform is a substantial challenge. Indeed, obtaining good performance requires a comprehensive understanding of all layers of the underlying platform, deep insight into the computation at hand, and the ingenuity and creativity required to obtain an effective mapping of the computation onto the machine. The reward for mastering these sophisticated and challenging topics is the ability to make computations that can process large amount of data orders of magnitude more quickly and efficiently and to obtain results that are unavailable with standard pr Modern computing platforms provide unprecedented amounts of raw computational power. But significant complexity comes along with this power, to the point that making useful computations exploit even a fraction of the potential of the computing platform is a substantial challenge. Indeed, obtaining good performance requires a comprehensive understanding of all layers of the underlying platform, deep insight into the computation at hand, and the ingenuity and creativity required to obtain an effective mapping of the computation onto the machine. The reward for mastering these sophisticated and challenging topics is the ability to make computations that can process large amount of data orders of magnitude more quickly and efficiently and to obtain results that are unavailable with standard prSubjects

performance engineering | performance engineering | parallelism | parallelism | computational power | computational power | complexity | complexity | computation | computation | efficiency | efficiency | high performance | high performance | software system | software system | performance analysis | performance analysis | algorithms | algorithms | instruction level optimization | instruction level optimization | cache | cache | memory | memory | parallel programming | parallel programming | distributed systems | distributed systems | algorithmic design | algorithmic design | profile | profile | multithreaded | multithreaded | cilk | cilk | cilk arts | cilk arts | ray tracer | ray tracer | render | renderLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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6.896 covers mathematical foundations of parallel hardware, from computer arithmetic to physical design, focusing on algorithmic underpinnings. Topics covered include: arithmetic circuits, parallel prefix, systolic arrays, retiming, clocking methodologies, boolean logic, sorting networks, interconnection networks, hypercubic networks, P-completeness, VLSI layout theory, reconfigurable wiring, fat-trees, and area-time complexity. This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5511 (Theory of Parallel Hardware). 6.896 covers mathematical foundations of parallel hardware, from computer arithmetic to physical design, focusing on algorithmic underpinnings. Topics covered include: arithmetic circuits, parallel prefix, systolic arrays, retiming, clocking methodologies, boolean logic, sorting networks, interconnection networks, hypercubic networks, P-completeness, VLSI layout theory, reconfigurable wiring, fat-trees, and area-time complexity. This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5511 (Theory of Parallel Hardware).Subjects

parallel hardware | parallel hardware | computer arithmetic | computer arithmetic | physical design | physical design | algorithms | algorithms | arithmetic circuits | arithmetic circuits | parallel prefix | parallel prefix | systolic arrays | systolic arrays | retiming | retiming | clocking methodologies | clocking methodologies | boolean logic | boolean logic | sorting networks | sorting networks | interconnection networks | interconnection networks | hypercubic networks | hypercubic networks | P-completeness | P-completeness | VLSI layout theory | VLSI layout theory | reconfigurable wiring | reconfigurable wiring | fat-trees | fat-trees | area-time complexity | area-time complexityLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata6.895 Theory of Parallel Systems (SMA 5509) (MIT) 6.895 Theory of Parallel Systems (SMA 5509) (MIT)

Description

6.895 covers theoretical foundations of general-purpose parallel computing systems, from languages to architecture. The focus is on the algorithmic underpinnings of parallel systems. The topics for the class will vary depending on student interest, but will likely include multithreading, synchronization, race detection, load balancing, memory consistency, routing networks, message-routing algorithms, and VLSI layout theory. The class will emphasize randomized algorithms and probabilistic analysis, including high-probability arguments. This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5509 (Theory of Parallel Systems). 6.895 covers theoretical foundations of general-purpose parallel computing systems, from languages to architecture. The focus is on the algorithmic underpinnings of parallel systems. The topics for the class will vary depending on student interest, but will likely include multithreading, synchronization, race detection, load balancing, memory consistency, routing networks, message-routing algorithms, and VLSI layout theory. The class will emphasize randomized algorithms and probabilistic analysis, including high-probability arguments. This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5509 (Theory of Parallel Systems).Subjects

parallel systems | parallel systems | parallel computing | parallel computing | algorithms | algorithms | multithreading | multithreading | synchronization | synchronization | race detection | race detection | load balancing | load balancing | memory consistency | memory consistency | routing networks | routing networks | message-routing algorithms | message-routing algorithms | VLSI layout theory | VLSI layout theory | randomized algorithms | randomized algorithms | probabilistic analysis | probabilistic analysis | high-probability arguments | high-probability argumentsLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata6.827 Multithreaded Parallelism: Languages and Compilers (MIT)

Description

The topics covered in this course include: Languages and compilers to exploit multithreaded parallelism Implicit parallel programming using functional languages and their extensions Higher-order functions, non-strictness, and polymorphism Explicit parallel programming and nondeterminism The lambda calculus and its variants Term rewriting and operational semantics Compiling multithreaded code for symmetric multiprocessors and clusters Static analysis and compiler optimizations This course is worth 4 Engineering Design Points.Subjects

languages | compilers | multithreaded parallelism | implicit parallel programming | higher order functions | non-strictness | polymorphism | explicit parallel programming | nondeterminism | lambda calculus | term rewriting | symmetric multiprocessors | clusters | static analysis | compiler optimizationsLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata6.830 Database Systems (MIT) 6.830 Database Systems (MIT)

Description

This course relies on primary readings from the database community to introduce graduate students to the foundations of database systems, focusing on basics such as the relational algebra and data model, schema normalization, query optimization, and transactions. It is designed for students who have taken MIT course 6.033 (or equivalent); no prior database experience is assumed though students who have taken an undergraduate course in databases are encouraged to attend. Topics related to the engineering and design of database systems, including: data models; database and schema design; schema normalization and integrity constraints; query processing; query optimization and cost estimation; transactions; recovery; concurrency control; isolation and consistency; distributed, parallel, and he This course relies on primary readings from the database community to introduce graduate students to the foundations of database systems, focusing on basics such as the relational algebra and data model, schema normalization, query optimization, and transactions. It is designed for students who have taken MIT course 6.033 (or equivalent); no prior database experience is assumed though students who have taken an undergraduate course in databases are encouraged to attend. Topics related to the engineering and design of database systems, including: data models; database and schema design; schema normalization and integrity constraints; query processing; query optimization and cost estimation; transactions; recovery; concurrency control; isolation and consistency; distributed, parallel, and heSubjects

engineering and design of database systems | data models | engineering and design of database systems | data models | database and schema design | database and schema design | schema normalization and integrity constraints | schema normalization and integrity constraints | query processing | query processing | query optimization and cost estimation | query optimization and cost estimation | transactions | transactions | recovery | recovery | concurrency control | concurrency control | isolation and consistency | isolation and consistency | distributed | distributed | parallel | parallel | heterogeneous databases | heterogeneous databases | adaptive databases | adaptive databases | trigger systems | trigger systems | pub-sub systems | pub-sub systems | semi structured data and XML querying | semi structured data and XML queryingLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata9.04 Neural Basis of Vision and Audition (MIT) 9.04 Neural Basis of Vision and Audition (MIT)

Description

Examines the neural bases of visual and auditory processing for perception and sensorimotor control. Focuses on physiological and anatomical studies of the mammalian nervous system as well as behavioral studies of animals and humans. Studies visual pattern, color and depth perception, auditory responses and speech coding, and spatial localization. Offered alternate years. Examines the neural bases of visual and auditory processing for perception and sensorimotor control. Focuses on physiological and anatomical studies of the mammalian nervous system as well as behavioral studies of animals and humans. Studies visual pattern, color and depth perception, auditory responses and speech coding, and spatial localization. Offered alternate years.Subjects

visual system | visual system | eye-movement control | eye-movement control | retina | retina | lateral geniculate nucleus | lateral geniculate nucleus | visual cortex | visual cortex | the parallel channels | the parallel channels | color | color | motion | motion | depth | depth | form | form | neural control | neural control | visually guided eye movements | visually guided eye movements | middle ear | middle ear | cochlear | cochlear | otoacoustic emissions | otoacoustic emissions | cochlear ultrastructure and neuroanatomy | cochlear ultrastructure and neuroanatomy | cochlear ion homeostasis and synaptic transmission | cochlear ion homeostasis and synaptic transmission | noise-induced and age-related hearing loss | noise-induced and age-related hearing loss | neural degeneration | neural degeneration | neurophysiological | neurophysiological | ascending | ascending | descending | descending | auditory pathways auditory nerve | auditory pathways auditory nerve | cochlear nucleus | cochlear nucleus | inferior colliculus | inferior colliculus | olivocochlear system | olivocochlear system | functional brain imaging | functional brain imaging | tinnitus | tinnitusLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata16.225 Computational Mechanics of Materials (MIT) 16.225 Computational Mechanics of Materials (MIT)

Description

16.225 is a graduate level course on Computational Mechanics of Materials. The primary focus of this course is on the teaching of state-of-the-art numerical methods for the analysis of the nonlinear continuum response of materials. The range of material behavior considered in this course includes: linear and finite deformation elasticity, inelasticity and dynamics. Numerical formulation and algorithms include: variational formulation and variational constitutive updates, finite element discretization, error estimation, constrained problems, time integration algorithms and convergence analysis. There is a strong emphasis on the (parallel) computer implementation of algorithms in programming assignments. The application to real engineering applications and problems in engineering science is 16.225 is a graduate level course on Computational Mechanics of Materials. The primary focus of this course is on the teaching of state-of-the-art numerical methods for the analysis of the nonlinear continuum response of materials. The range of material behavior considered in this course includes: linear and finite deformation elasticity, inelasticity and dynamics. Numerical formulation and algorithms include: variational formulation and variational constitutive updates, finite element discretization, error estimation, constrained problems, time integration algorithms and convergence analysis. There is a strong emphasis on the (parallel) computer implementation of algorithms in programming assignments. The application to real engineering applications and problems in engineering science isSubjects

Computational Mechanics | Computational Mechanics | Computation | Computation | Mechanics | Mechanics | Materials | Materials | Numerical Methods | Numerical Methods | Numerical | Numerical | Nonlinear Continuum Response | Nonlinear Continuum Response | Continuum | Continuum | Deformation | Deformation | Elasticity | Elasticity | Inelasticity | Inelasticity | Dynamics | Dynamics | Variational Formulation | Variational Formulation | Variational Constitutive Updates | Variational Constitutive Updates | Finite Element | Finite Element | Discretization | Discretization | Error Estimation | Error Estimation | Constrained Problems | Constrained Problems | Time Integration | Time Integration | Convergence Analysis | Convergence Analysis | Programming | Programming | Continuum Response | Continuum Response | Computational | Computational | state-of-the-art | state-of-the-art | methods | methods | modeling | modeling | simulation | simulation | mechanical | mechanical | response | response | engineering | engineering | aerospace | aerospace | civil | civil | material | material | science | science | biomechanics | biomechanics | behavior | behavior | finite | finite | deformation | deformation | elasticity | elasticity | inelasticity | inelasticity | contact | contact | friction | friction | coupled | coupled | numerical | numerical | formulation | formulation | algorithms | algorithms | Variational | Variational | constitutive | constitutive | updates | updates | element | element | discretization | discretization | mesh | mesh | generation | generation | error | error | estimation | estimation | constrained | constrained | problems | problems | time | time | convergence | convergence | analysis | analysis | parallel | parallel | computer | computer | implementation | implementation | programming | programming | assembly | assembly | equation-solving | equation-solving | formulating | formulating | implementing | implementing | complex | complex | approximations | approximations | equations | equations | motion | motion | dynamic | dynamic | deformations | deformations | continua | continua | plasticity | plasticity | rate-dependency | rate-dependency | integration | integrationLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata6.823 Computer System Architecture (MIT) 6.823 Computer System Architecture (MIT)

Description

6.823 is a study of the evolution of computer architecture and the factors influencing the design of hardware and software elements of computer systems. Topics may include: instruction set design; processor micro-architecture and pipelining; cache and virtual memory organizations; protection and sharing; I/O and interrupts; in-order and out-of-order superscalar architectures; VLIW machines; vector supercomputers; multithreaded architectures; symmetric multiprocessors; and parallel computers. 6.823 is a study of the evolution of computer architecture and the factors influencing the design of hardware and software elements of computer systems. Topics may include: instruction set design; processor micro-architecture and pipelining; cache and virtual memory organizations; protection and sharing; I/O and interrupts; in-order and out-of-order superscalar architectures; VLIW machines; vector supercomputers; multithreaded architectures; symmetric multiprocessors; and parallel computers.Subjects

computer architecture | | computer architecture | | computer system architecture | | computer system architecture | | hardware | | hardware | | hardware design | | hardware design | | software | | software | | software design | | software design | | instruction set design | | instruction set design | | processor micro-architecture | | processor micro-architecture | | pipelining | | pipelining | | cache memory | | cache memory | | irtual memory | | irtual memory | | I/O | | I/O | | input/output | | input/output | | interrupts | | interrupts | | superscalar architectures | | superscalar architectures | | VLIW machines | | VLIW machines | | vector supercomputers | | vector supercomputers | | multithreaded architectures | | multithreaded architectures | | symmetric multiprocessors | | symmetric multiprocessors | | parallel computers | parallel computers | computer architecture | computer architecture | computer system architecture | computer system architecture | hardware | hardware | hardware design | hardware design | software | software | software design | software design | instruction set design | instruction set design | processor micro-architecture | processor micro-architecture | pipelining | pipelining | cache memory | cache memory | virtual memory | virtual memory | I/O | I/O | input/output | input/output | interrupts | interrupts | superscalar architectures | superscalar architectures | VLIW machines | VLIW machines | vector supercomputers | vector supercomputers | multithreaded architectures | multithreaded architectures | symmetric multiprocessors | symmetric multiprocessorsLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata6.189 Multicore Programming Primer (MIT) 6.189 Multicore Programming Primer (MIT)

Description

Includes audio/video content: AV lectures, AV special element video, AV special element video. The course serves as an introductory course in parallel programming. It offers a series of lectures on parallel programming concepts as well as a group project providing hands-on experience with parallel programming. The students will have the unique opportunity to use the cutting-edge PLAYSTATION 3 development platform as they learn how to design and implement exciting applications for multicore architectures. At the end of the course, students will have an understanding of: Fundamental design philosophies that multicore architectures address. Parallel programming philosophies and emerging best practices. This course is offered during the Independent Activities Period (IAP), which is a specia Includes audio/video content: AV lectures, AV special element video, AV special element video. The course serves as an introductory course in parallel programming. It offers a series of lectures on parallel programming concepts as well as a group project providing hands-on experience with parallel programming. The students will have the unique opportunity to use the cutting-edge PLAYSTATION 3 development platform as they learn how to design and implement exciting applications for multicore architectures. At the end of the course, students will have an understanding of: Fundamental design philosophies that multicore architectures address. Parallel programming philosophies and emerging best practices. This course is offered during the Independent Activities Period (IAP), which is a speciaSubjects

multicore architectures | multicore architectures | parallel programming patterns | parallel programming patterns | Sony PlayStation 3 | Sony PlayStation 3 | competition | competitionLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata6.002 Circuits and Electronics (MIT) 6.002 Circuits and Electronics (MIT)

Description

6.002 introduces the fundamentals of the lumped circuit abstraction. Topics covered include: resistive elements and networks; independent and dependent sources; switches and MOS transistors; digital abstraction; amplifiers; energy storage elements; dynamics of first- and second-order networks; design in the time and frequency domains; and analog and digital circuits and applications. Design and lab exercises are also significant components of the course. 6.002 is worth 4 Engineering Design Points. 6.002 introduces the fundamentals of the lumped circuit abstraction. Topics covered include: resistive elements and networks; independent and dependent sources; switches and MOS transistors; digital abstraction; amplifiers; energy storage elements; dynamics of first- and second-order networks; design in the time and frequency domains; and analog and digital circuits and applications. Design and lab exercises are also significant components of the course. 6.002 is worth 4 Engineering Design Points.Subjects

circuit | circuit | electronic | electronic | abstraction | abstraction | lumped circuit | lumped circuit | digital | digital | amplifier | amplifier | differential equations | differential equations | time behavior | time behavior | energy storage | energy storage | semiconductor diode | semiconductor diode | field-effect | field-effect | field-effect transistor | field-effect transistor | resistor | resistor | source | source | inductor | inductor | capacitor | capacitor | diode | diode | series-parallel reduction | series-parallel reduction | voltage | voltage | current divider | current divider | node method | node method | linearity | linearity | superposition | superposition | Thevenin-Norton equivalent | Thevenin-Norton equivalent | power flow | power flow | Boolean algebra | Boolean algebra | binary signal | binary signal | MOSFET | MOSFET | noise margin | noise margin | singularity functions | singularity functions | sinusoidal-steady-state | sinusoidal-steady-state | impedance | impedance | frequency response curves | frequency response curves | operational amplifier | operational amplifier | Op-Amp | Op-Amp | negative feedback | negative feedback | positive feedback | positive feedbackLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata6.172 Performance Engineering of Software Systems (MIT)

Description

Modern computing platforms provide unprecedented amounts of raw computational power. But significant complexity comes along with this power, to the point that making useful computations exploit even a fraction of the potential of the computing platform is a substantial challenge. Indeed, obtaining good performance requires a comprehensive understanding of all layers of the underlying platform, deep insight into the computation at hand, and the ingenuity and creativity required to obtain an effective mapping of the computation onto the machine. The reward for mastering these sophisticated and challenging topics is the ability to make computations that can process large amount of data orders of magnitude more quickly and efficiently and to obtain results that are unavailable with standard prSubjects

performance engineering | parallelism | computational power | complexity | computation | efficiency | high performance | software system | performance analysis | algorithms | instruction level optimization | cache | memory | parallel programming | distributed systems | algorithmic design | profile | multithreaded | cilk | cilk arts | ray tracer | renderLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata6.896 Theory of Parallel Hardware (SMA 5511) (MIT)

Description

6.896 covers mathematical foundations of parallel hardware, from computer arithmetic to physical design, focusing on algorithmic underpinnings. Topics covered include: arithmetic circuits, parallel prefix, systolic arrays, retiming, clocking methodologies, boolean logic, sorting networks, interconnection networks, hypercubic networks, P-completeness, VLSI layout theory, reconfigurable wiring, fat-trees, and area-time complexity. This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5511 (Theory of Parallel Hardware).Subjects

parallel hardware | computer arithmetic | physical design | algorithms | arithmetic circuits | parallel prefix | systolic arrays | retiming | clocking methodologies | boolean logic | sorting networks | interconnection networks | hypercubic networks | P-completeness | VLSI layout theory | reconfigurable wiring | fat-trees | area-time complexityLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata6.895 Theory of Parallel Systems (SMA 5509) (MIT)

Description

6.895 covers theoretical foundations of general-purpose parallel computing systems, from languages to architecture. The focus is on the algorithmic underpinnings of parallel systems. The topics for the class will vary depending on student interest, but will likely include multithreading, synchronization, race detection, load balancing, memory consistency, routing networks, message-routing algorithms, and VLSI layout theory. The class will emphasize randomized algorithms and probabilistic analysis, including high-probability arguments. This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5509 (Theory of Parallel Systems).Subjects

parallel systems | parallel computing | algorithms | multithreading | synchronization | race detection | load balancing | memory consistency | routing networks | message-routing algorithms | VLSI layout theory | randomized algorithms | probabilistic analysis | high-probability argumentsLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata9.04 Neural Basis of Vision and Audtion (MIT) 9.04 Neural Basis of Vision and Audtion (MIT)

Description

This course is designed to ground the undergraduate student in the fields of vision and audition, which includes both speech and hearing. The neural bases of visual and auditory processing for perception and sensorimotor control is examined. Topics focus on physiological and anatomical studies of the mammalian nervous system as well as behavioral studies of animals and humans. Studies in visual pattern, color and depth perception, auditory responses and speech coding, and spatial localization are also covered. This course is designed to ground the undergraduate student in the fields of vision and audition, which includes both speech and hearing. The neural bases of visual and auditory processing for perception and sensorimotor control is examined. Topics focus on physiological and anatomical studies of the mammalian nervous system as well as behavioral studies of animals and humans. Studies in visual pattern, color and depth perception, auditory responses and speech coding, and spatial localization are also covered.Subjects

visual system | visual system | eye-movement control | eye-movement control | retina | retina | lateral geniculate nucleus | lateral geniculate nucleus | visual cortex | visual cortex | the parallel channels | the parallel channels | color | color | motion | motion | depth | depth | form | form | neural control | neural control | visually guided eye movements | visually guided eye movements | middle ear | middle ear | cochlear | cochlear | otoacoustic emissions | otoacoustic emissions | cochlear ultrastructure and neuroanatomy | cochlear ultrastructure and neuroanatomy | cochlear ion homeostasis and synaptic transmission | cochlear ion homeostasis and synaptic transmission | noise-induced and age-related hearing loss | noise-induced and age-related hearing loss | neural degeneration | neural degeneration | neurophysiological | neurophysiological | ascending | ascending | descending | descending | auditory pathways auditory nerve | auditory pathways auditory nerve | cochlear nucleus | cochlear nucleus | inferior colliculus | inferior colliculus | olivocochlear system | olivocochlear system | functional brain imaging | functional brain imaging | tinnitus | tinnitusLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata6.854J Advanced Algorithms (MIT)

Description

This course is a first-year graduate course in algorithms. Emphasis is placed on fundamental algorithms and advanced methods of algorithmic design, analysis, and implementation. Techniques to be covered include amortization, randomization, fingerprinting, word-level parallelism, bit scaling, dynamic programming, network flow, linear programming, fixed-parameter algorithms, and approximation algorithms. Domains include string algorithms, network optimization, parallel algorithms, computational geometry, online algorithms, external memory, cache, and streaming algorithms, and data structures.Subjects

amortization | randomization | fingerprinting | word-level parallelism | bit scaling | dynamic programming | network flow | linear programming | fixed-parameter algorithms | approximation algorithms | string algorithms | network optimization | parallel algorithms | computational geometry | online algorithms | external memory | external cache | external streaming | data structuresLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htmSite sourced from

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This course covers topics on the engineering of computer software and hardware systems: techniques for controlling complexity; strong modularity using client-server design, virtual memory, and threads; networks; atomicity and coordination of parallel activities; recovery and reliability; privacy, security, and encryption; and impact of computer systems on society. We will also look at case studies of working systems and readings from the current literature provide comparisons and contrasts, and do two design projects. Students engage in extensive written communication exercises. Enrollment may be limited. This course is worth 4 Engineering Design Points.This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5501 (Computer System Engineering). This course covers topics on the engineering of computer software and hardware systems: techniques for controlling complexity; strong modularity using client-server design, virtual memory, and threads; networks; atomicity and coordination of parallel activities; recovery and reliability; privacy, security, and encryption; and impact of computer systems on society. We will also look at case studies of working systems and readings from the current literature provide comparisons and contrasts, and do two design projects. Students engage in extensive written communication exercises. Enrollment may be limited. This course is worth 4 Engineering Design Points.This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5501 (Computer System Engineering).Subjects

computer software | computer software | hardware systems | hardware systems | controlling complexity | controlling complexity | strong modularity | strong modularity | client-server design | client-server design | virtual memory | virtual memory | threads | threads | networks | networks | atomicity | atomicity | coordination | coordination | parallel activities | parallel activities | recovery | recovery | reliability | reliability | privacy | privacy | security | security | encryption | encryption | impact on society | impact on society | computer systems | computer systems | case studies | case studiesLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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Includes audio/video content: AV lectures. This course teaches techniques for the design and analysis of efficient algorithms, emphasizing methods useful in practice. Topics covered include: sorting; search trees, heaps, and hashing; divide-and-conquer; dynamic programming; amortized analysis; graph algorithms; shortest paths; network flow; computational geometry; number-theoretic algorithms; polynomial and matrix calculations; caching; and parallel computing.This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5503 (Analysis and Design of Algorithms). Includes audio/video content: AV lectures. This course teaches techniques for the design and analysis of efficient algorithms, emphasizing methods useful in practice. Topics covered include: sorting; search trees, heaps, and hashing; divide-and-conquer; dynamic programming; amortized analysis; graph algorithms; shortest paths; network flow; computational geometry; number-theoretic algorithms; polynomial and matrix calculations; caching; and parallel computing.This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5503 (Analysis and Design of Algorithms).Subjects

algorithms | algorithms | efficient algorithms | efficient algorithms | sorting | sorting | search trees | search trees | heaps | heaps | hashing | hashing | divide-and-conquer | divide-and-conquer | dynamic programming | dynamic programming | amortized analysis | amortized analysis | graph algorithms | graph algorithms | shortest paths | shortest paths | network flow | network flow | computational geometry | computational geometry | number-theoretic algorithms | number-theoretic algorithms | polynomial and matrix calculations | polynomial and matrix calculations | caching | caching | parallel computing | parallel computingLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata6.830 Database Systems (MIT) 6.830 Database Systems (MIT)

Description

This course relies on primary readings from the database community to introduce graduate students to the foundations of database systems, focusing on basics such as the relational algebra and data model, schema normalization, query optimization, and transactions. It is designed for students who have taken 6.033 (or equivalent); no prior database experience is assumed, though students who have taken an undergraduate course in databases are encouraged to attend. This course relies on primary readings from the database community to introduce graduate students to the foundations of database systems, focusing on basics such as the relational algebra and data model, schema normalization, query optimization, and transactions. It is designed for students who have taken 6.033 (or equivalent); no prior database experience is assumed, though students who have taken an undergraduate course in databases are encouraged to attend.Subjects

database systems | database systems | relational algebra | relational algebra | data model | data model | query optimization | query optimization | query processing | query processing | transactions | transactions | recovery | recovery | concurrency control | concurrency control | distributed transactions | distributed transactions | parallel databases | parallel databases | scientific databases | scientific databases | streaming databases | streaming databasesLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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This course teaches techniques for the design and analysis of efficient algorithms, emphasizing methods useful in practice. Topics covered include: sorting; search trees, heaps, and hashing; divide-and-conquer; dynamic programming; amortized analysis; graph algorithms; shortest paths; network flow; computational geometry; number-theoretic algorithms; polynomial and matrix calculations; caching; and parallel computing.This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5503 (Analysis and Design of Algorithms). This course teaches techniques for the design and analysis of efficient algorithms, emphasizing methods useful in practice. Topics covered include: sorting; search trees, heaps, and hashing; divide-and-conquer; dynamic programming; amortized analysis; graph algorithms; shortest paths; network flow; computational geometry; number-theoretic algorithms; polynomial and matrix calculations; caching; and parallel computing.This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5503 (Analysis and Design of Algorithms).Subjects

algorithms | algorithms | efficient algorithms | efficient algorithms | sorting | sorting | search trees | search trees | heaps | heaps | hashing | hashing | divide-and-conquer | divide-and-conquer | dynamic programming | dynamic programming | amortized analysis | amortized analysis | graph algorithms | graph algorithms | shortest paths | shortest paths | network flow | network flow | computational geometry | computational geometry | number-theoretic algorithms | number-theoretic algorithms | polynomial and matrix calculations | polynomial and matrix calculations | caching | caching | parallel computing | parallel computing | SMA 5503 | SMA 5503 | 6.046 | 6.046License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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