This document discusses using graphics processing units (GPUs) to crack passwords through CUDA cracking. It provides instructions on building a CUDA machine with multiple GPUs, selecting GPUs with many CUDA cores for best performance, and describes several password cracking tools that support GPU acceleration on Windows and Linux operating systems. Examples are given demonstrating that GPU cracking can crack a simple MD5 hash hundreds of times faster than CPU-only cracking.
The document discusses setting up FreeBSD on DigitalOcean virtual private servers (VPS). It provides details on DigitalOcean's pricing plans and features for droplets. It then describes the author's experience deploying FreeBSD 10.1 and FreeBSD AMP 10.1 droplets on DigitalOcean, including summaries of dmesg output and installed packages.
This document provides an overview of CUDA debugging concepts including:
1. CUDA programming and execution model with host/device functions and compiling process
2. CUDA memory architecture including types like shared, global, constant memory
3. CUDA exception list including illegal address, stack overflow, illegal instruction exceptions
4. CUDA debugging techniques like CUDA-gdb, printf, asserts, and memory debugging tools
It discusses key CUDA terminology such as host, device, kernel, SM, block, thread, warp and lane. The document outlines debugging of kernels and memory as well as tools for checking memory errors, races, initialization and synchronization issues. Future work areas are also mentioned.
This document provides an overview of CUDA (Compute Unified Device Architecture), NVIDIA's parallel computing platform and programming model that allows software developers to leverage the parallel compute engines in NVIDIA GPUs. The document discusses key aspects of CUDA including: GPU hardware architecture with many scalar processors and concurrent threads; the CUDA programming model with host CPU code calling parallel kernels that execute across multiple GPU threads; memory hierarchies and data transfers between host and device memory; and programming basics like compiling with nvcc, allocating and copying data between host and device memory.
LAS16-403: GDB Linux Kernel Awareness
Speakers: Peter Griffin
Date: September 29, 2016
★ Session Description ★
The presentation will look at the ways in which GDB can be enhanced when debugging the Linux kernel to give it better knowledge of the underlying operating system to enable a better debugging experience. It will also provide a status of the current work being undertaken in this area by the ST landing team, a demo and potential future work.
★ Resources ★
Etherpad: pad.linaro.org/p/las16-403
Presentations & Videos: http://connect.linaro.org/resource/las16/las16-403/
★ Event Details ★
Linaro Connect Las Vegas 2016 – #LAS16
September 26-30, 2016
http://www.linaro.org
http://connect.linaro.org
Bringing up Android on your favorite X86 Workstation or VM (AnDevCon Boston, ...Ron Munitz
My session at AnDevCon Bostong, May 2013, Boston, MA.
This class introduces the concepts of AOSP and how to use it in order to configure and build one of the most popular Android devices available: The Android emulator, for an x86 target. You will then learn a reincarnation of the AOSP, intended to bring Android to as many x86 devices as possible. You will see its structure and compare it with the AOSP, and demonstrate how such a build works within Virtual Box, QEMU and more.
LEVEL: Intermediate
TOPIC AREA: Embedded Android
For Training/Consulting requests: info@thepscg.com
The document provides an overview of introductory GPGPU programming with CUDA. It discusses why GPUs are useful for parallel computing applications due to their high FLOPS and memory bandwidth capabilities. It then outlines the CUDA programming model, including launching kernels on the GPU with grids and blocks of threads, and memory management between CPU and GPU. As an example, it walks through a simple matrix multiplication problem implemented on the CPU and GPU to illustrate CUDA programming concepts.
A beginner’s guide to programming GPUs with CUDAPiyush Mittal
This document provides an overview of GPU programming with CUDA. It defines what a GPU is, that it has many compute cores for graphics processing. It explains that CUDA extends C to access GPU capabilities, allowing for parallel execution across GPU threads. It provides examples of CUDA code structure and keywords to specify where code runs and launch kernels. Performance considerations include data storage, shared memory, and efficient thread scheduling.
This document contains a database of graphics cards and their Thermal Design Power (TDP) ratings from various manufacturers including NVIDIA and AMD. It lists over 200 graphics card models ranging from older models like the GeForce 8800 up to newer models like the GTX 980. Each entry shows the graphics card model and its TDP rating in watts according to the source. The database aims to provide a comprehensive listing of TDP values for modern graphics cards to help understand their power consumption.
The document discusses setting up FreeBSD on DigitalOcean virtual private servers (VPS). It provides details on DigitalOcean's pricing plans and features for droplets. It then describes the author's experience deploying FreeBSD 10.1 and FreeBSD AMP 10.1 droplets on DigitalOcean, including summaries of dmesg output and installed packages.
This document provides an overview of CUDA debugging concepts including:
1. CUDA programming and execution model with host/device functions and compiling process
2. CUDA memory architecture including types like shared, global, constant memory
3. CUDA exception list including illegal address, stack overflow, illegal instruction exceptions
4. CUDA debugging techniques like CUDA-gdb, printf, asserts, and memory debugging tools
It discusses key CUDA terminology such as host, device, kernel, SM, block, thread, warp and lane. The document outlines debugging of kernels and memory as well as tools for checking memory errors, races, initialization and synchronization issues. Future work areas are also mentioned.
This document provides an overview of CUDA (Compute Unified Device Architecture), NVIDIA's parallel computing platform and programming model that allows software developers to leverage the parallel compute engines in NVIDIA GPUs. The document discusses key aspects of CUDA including: GPU hardware architecture with many scalar processors and concurrent threads; the CUDA programming model with host CPU code calling parallel kernels that execute across multiple GPU threads; memory hierarchies and data transfers between host and device memory; and programming basics like compiling with nvcc, allocating and copying data between host and device memory.
LAS16-403: GDB Linux Kernel Awareness
Speakers: Peter Griffin
Date: September 29, 2016
★ Session Description ★
The presentation will look at the ways in which GDB can be enhanced when debugging the Linux kernel to give it better knowledge of the underlying operating system to enable a better debugging experience. It will also provide a status of the current work being undertaken in this area by the ST landing team, a demo and potential future work.
★ Resources ★
Etherpad: pad.linaro.org/p/las16-403
Presentations & Videos: http://connect.linaro.org/resource/las16/las16-403/
★ Event Details ★
Linaro Connect Las Vegas 2016 – #LAS16
September 26-30, 2016
http://www.linaro.org
http://connect.linaro.org
Bringing up Android on your favorite X86 Workstation or VM (AnDevCon Boston, ...Ron Munitz
My session at AnDevCon Bostong, May 2013, Boston, MA.
This class introduces the concepts of AOSP and how to use it in order to configure and build one of the most popular Android devices available: The Android emulator, for an x86 target. You will then learn a reincarnation of the AOSP, intended to bring Android to as many x86 devices as possible. You will see its structure and compare it with the AOSP, and demonstrate how such a build works within Virtual Box, QEMU and more.
LEVEL: Intermediate
TOPIC AREA: Embedded Android
For Training/Consulting requests: info@thepscg.com
The document provides an overview of introductory GPGPU programming with CUDA. It discusses why GPUs are useful for parallel computing applications due to their high FLOPS and memory bandwidth capabilities. It then outlines the CUDA programming model, including launching kernels on the GPU with grids and blocks of threads, and memory management between CPU and GPU. As an example, it walks through a simple matrix multiplication problem implemented on the CPU and GPU to illustrate CUDA programming concepts.
A beginner’s guide to programming GPUs with CUDAPiyush Mittal
This document provides an overview of GPU programming with CUDA. It defines what a GPU is, that it has many compute cores for graphics processing. It explains that CUDA extends C to access GPU capabilities, allowing for parallel execution across GPU threads. It provides examples of CUDA code structure and keywords to specify where code runs and launch kernels. Performance considerations include data storage, shared memory, and efficient thread scheduling.
This document contains a database of graphics cards and their Thermal Design Power (TDP) ratings from various manufacturers including NVIDIA and AMD. It lists over 200 graphics card models ranging from older models like the GeForce 8800 up to newer models like the GTX 980. Each entry shows the graphics card model and its TDP rating in watts according to the source. The database aims to provide a comprehensive listing of TDP values for modern graphics cards to help understand their power consumption.
This document is a user manual for HD Doctor for WD from SalvationDATA Laboratory. It contains information about identifying different types of Western Digital hard drives, common malfunctions, module lists, and instructions for installing and using the HD Doctor software to repair drives and recover data. The manual has multiple chapters that provide details on the software interface, functions for firmware and module operations, self-scanning tests, and case studies for solving various error conditions.
This document provides performance results for various CUDA samples programs using an NVIDIA GeForce GTX 560 Ti GPU. It tests programs for concurrent kernels, conjugate gradient, convolution using FFTs, separable convolution, CUDA integration with C++ and decoding video to OpenGL and DirectX. Frame rates for video decoding ranged from 723-1031 fps. Convolution tests showed throughput of up to 1588 MPix/s. Conjugate gradient achieved convergence within 8 iterations.
The document provides an overview of GPU computing and CUDA programming. It discusses how GPUs enable massively parallel and affordable computing through their manycore architecture. The CUDA programming model allows developers to accelerate applications by launching parallel kernels on the GPU from their existing C/C++ code. Kernels contain many concurrent threads that execute the same code on different data. CUDA features a memory hierarchy and runtime for managing GPU memory and launching kernels. Overall, the document introduces GPU and CUDA concepts for general-purpose parallel programming on NVIDIA GPUs.
The document describes pcDuino, a $39 single board computer compatible with the Arduino ecosystem. It has 1GB RAM, 4GB flash storage, Gigabit Ethernet, and runs Linux and Android. The document outlines different pcDuino models and their specifications. It provides examples of programming pcDuino using languages like Scratch, C, Python, Go, and through IDEs like Arduino and Cloud 9. Accessories like shields can expand its functionality for hardware experiments.
Highlighted notes while studying Concurrent Data Structures:
GDDR5 SDRAM
Source: Wikipedia
GDDR5 SDRAM, an abbreviation for Graphics Double Data Rate 5 Synchronous Dynamic Random-Access Memory, is a modern type of synchronous graphics random-access memory (SGRAM) with a high bandwidth ("double data rate") interface designed for use in graphics cards, game consoles, and high-performance computing. [1] It is a type of GDDR SDRAM (graphics DDR SDRAM).
Wikipedia is a free online encyclopedia, created and edited by volunteers around the world and hosted by the Wikimedia Foundation.
Hands on Virtualization with Ganeti (part 1) - LinuxCon 2012Lance Albertson
This document is part 1 of a presentation on virtualization with Ganeti. It introduces Ganeti as virtual machine management software that manages clusters of physical machines running Xen, KVM, or LXC. It discusses Ganeti's components, architecture, features like live migration and failure recovery using DRBD, and how it is used at OSU Open Source Lab to power hundreds of VMs. The presentation then demonstrates initializing a Ganeti cluster, adding nodes and instances, and recovering from failures before opening for questions.
In this PowerPoint, learn how a security policy can be your first line of defense. Servers running AIX and other operating systems are frequent targets of cyberattacks, according to the Data Breach Investigations Report. From DoS attacks to malware, attackers have a variety of strategies at their disposal. Having a security policy in place makes it easier to ensure you have appropriate controls in place to protect mission-critical data.
The document discusses Compute Unified Device Architecture (CUDA), which is a parallel computing platform and programming model created by Nvidia that allows software developers to use GPUs for general-purpose processing. It provides an overview of CUDA, including its execution model, implementation details, applications, and advantages/drawbacks. The document also covers CUDA programming, compiling CUDA code, CUDA architectures, and concludes that CUDA has brought significant innovations to high performance computing.
This document provides an overview of CUDA (Compute Unified Device Architecture) and GPU programming. It begins with definitions of CUDA and GPU hardware architecture. The history of GPU development from basic graphics cards to modern programmable GPUs is discussed. The document then covers the CUDA programming model including the device model with multiprocessors and threads, and the execution model with grids, blocks and threads. It includes a code example to calculate squares on the GPU. Performance results are shown for different GPUs on a radix sort algorithm. The document concludes that GPU computing is powerful and will continue growing in importance for applications.
CUDA by Example : Getting Started : NotesSubhajit Sahu
Highlighted notes of:
Chapter 2: Getting Started
Book:
CUDA by Example
An Introduction to General Purpose GPU Computing
Authors:
Jason Sanders
Edward Kandrot
“This book is required reading for anyone working with accelerator-based computing systems.”
–From the Foreword by Jack Dongarra, University of Tennessee and Oak Ridge National Laboratory
CUDA is a computing architecture designed to facilitate the development of parallel programs. In conjunction with a comprehensive software platform, the CUDA Architecture enables programmers to draw on the immense power of graphics processing units (GPUs) when building high-performance applications. GPUs, of course, have long been available for demanding graphics and game applications. CUDA now brings this valuable resource to programmers working on applications in other domains, including science, engineering, and finance. No knowledge of graphics programming is required–just the ability to program in a modestly extended version of C.
CUDA by Example, written by two senior members of the CUDA software platform team, shows programmers how to employ this new technology. The authors introduce each area of CUDA development through working examples. After a concise introduction to the CUDA platform and architecture, as well as a quick-start guide to CUDA C, the book details the techniques and trade-offs associated with each key CUDA feature. You’ll discover when to use each CUDA C extension and how to write CUDA software that delivers truly outstanding performance.
Table of Contents
Why CUDA? Why Now?
Getting Started
Introduction to CUDA C
Parallel Programming in CUDA C
Thread Cooperation
Constant Memory and Events
Texture Memory
Graphics Interoperability
Atomics
Streams
CUDA C on Multiple GPUs
The Final Countdown
All the CUDA software tools you’ll need are freely available for download from NVIDIA.
Jason Sanders is a senior software engineer in NVIDIA’s CUDA Platform Group, helped develop early releases of CUDA system software and contributed to the OpenCL 1.0 Specification, an industry standard for heterogeneous computing. He has held positions at ATI Technologies, Apple, and Novell.
Edward Kandrot is a senior software engineer on NVIDIA’s CUDA Algorithms team, has more than twenty years of industry experience optimizing code performance for firms including Adobe, Microsoft, Google, and Autodesk.
This is a presentation that looks ta some of the Linux commands you could use to identify the hardware on your system. This can be useful for troubleshooting, or just for figuring out which motherboard is in which box.
Using GPUs to handle Big Data with Java by Adam Roberts.J On The Beach
Modern graphics processing units (GPUs) are efficient general-purpose stream processors. Learn how Java can exploit the power of GPUs to optimize high-performance enterprise and technical computing applications such as big data and analytics workloads. This presentation covers principles and considerations for GPU programming from Java and looks at the software stack and developer tools available. It also presents a demo showing GPU acceleration and discusses what is coming in the future.
Graphics processing unit or GPU (also occasionally called visual processing unit or VPU) is a specialized microprocessor that offloads and accelerates graphics rendering from the central (micro) processor. Modern GPUs are very efficient at manipulating computer graphics, and their highly parallel structure makes them more effective than general-purpose CPUs for a range of complex algorithms. In CPU, only a fraction of the chip does computations where as the GPU devotes more transistors to data processing.
GPGPU is a programming methodology based on modifying algorithms to run on existing GPU hardware for increased performance. Unfortunately, GPGPU programming is significantly more complex than traditional programming for several reasons.
The document discusses cache memory, CPU addressing modes, comparing processors, and GPUs. It provides information on cache memory levels L1, L2, and L3 and their characteristics. It describes different addressing modes used by CPUs like direct, indirect, indexed, and relative addressing. When comparing processors, the document advises checking clock speed, core performance, cache size, and benchmark results. It also outlines the differences between integrated and discrete GPUs and their uses for basic versus demanding graphics tasks.
A presentation for all the IT resellers and retailers in Nepal.
Introducing next generation technologies into the consumer market to collectively deliver a greater and richer computer experience.
Hardware refers to all of the physical parts of a computer system. F.pdfanjaniar7gallery
The document provides information about various hardware components of a computer system, including RAM, hard drives, graphics systems, and installing/upgrading these components. RAM is temporary memory that improves performance when more is installed. Hard drives store long-term data, and upgrading to a larger or solid state drive can speed up a computer. Graphics systems handle visual output, and a dedicated graphics card provides better performance for gaming and video editing than integrated graphics. Installing or upgrading RAM, graphics cards, and hard drives involves opening the computer case, inserting/connecting the new component, and ensuring proper installation through software.
Hardware for deep learning includes CPUs, GPUs, FPGAs, and ASICs. CPUs are general purpose but support deep learning through instructions like AVX-512 and libraries. GPUs like NVIDIA and AMD models are commonly used due to high parallelism and memory bandwidth. FPGAs offer high efficiency but require specialized programming. ASICs like Google's TPU are customized for deep learning and provide high performance but limited flexibility. Emerging hardware aims to improve efficiency and better match neural network computations.
This document is a user manual for HD Doctor for WD from SalvationDATA Laboratory. It contains information about identifying different types of Western Digital hard drives, common malfunctions, module lists, and instructions for installing and using the HD Doctor software to repair drives and recover data. The manual has multiple chapters that provide details on the software interface, functions for firmware and module operations, self-scanning tests, and case studies for solving various error conditions.
This document provides performance results for various CUDA samples programs using an NVIDIA GeForce GTX 560 Ti GPU. It tests programs for concurrent kernels, conjugate gradient, convolution using FFTs, separable convolution, CUDA integration with C++ and decoding video to OpenGL and DirectX. Frame rates for video decoding ranged from 723-1031 fps. Convolution tests showed throughput of up to 1588 MPix/s. Conjugate gradient achieved convergence within 8 iterations.
The document provides an overview of GPU computing and CUDA programming. It discusses how GPUs enable massively parallel and affordable computing through their manycore architecture. The CUDA programming model allows developers to accelerate applications by launching parallel kernels on the GPU from their existing C/C++ code. Kernels contain many concurrent threads that execute the same code on different data. CUDA features a memory hierarchy and runtime for managing GPU memory and launching kernels. Overall, the document introduces GPU and CUDA concepts for general-purpose parallel programming on NVIDIA GPUs.
The document describes pcDuino, a $39 single board computer compatible with the Arduino ecosystem. It has 1GB RAM, 4GB flash storage, Gigabit Ethernet, and runs Linux and Android. The document outlines different pcDuino models and their specifications. It provides examples of programming pcDuino using languages like Scratch, C, Python, Go, and through IDEs like Arduino and Cloud 9. Accessories like shields can expand its functionality for hardware experiments.
Highlighted notes while studying Concurrent Data Structures:
GDDR5 SDRAM
Source: Wikipedia
GDDR5 SDRAM, an abbreviation for Graphics Double Data Rate 5 Synchronous Dynamic Random-Access Memory, is a modern type of synchronous graphics random-access memory (SGRAM) with a high bandwidth ("double data rate") interface designed for use in graphics cards, game consoles, and high-performance computing. [1] It is a type of GDDR SDRAM (graphics DDR SDRAM).
Wikipedia is a free online encyclopedia, created and edited by volunteers around the world and hosted by the Wikimedia Foundation.
Hands on Virtualization with Ganeti (part 1) - LinuxCon 2012Lance Albertson
This document is part 1 of a presentation on virtualization with Ganeti. It introduces Ganeti as virtual machine management software that manages clusters of physical machines running Xen, KVM, or LXC. It discusses Ganeti's components, architecture, features like live migration and failure recovery using DRBD, and how it is used at OSU Open Source Lab to power hundreds of VMs. The presentation then demonstrates initializing a Ganeti cluster, adding nodes and instances, and recovering from failures before opening for questions.
In this PowerPoint, learn how a security policy can be your first line of defense. Servers running AIX and other operating systems are frequent targets of cyberattacks, according to the Data Breach Investigations Report. From DoS attacks to malware, attackers have a variety of strategies at their disposal. Having a security policy in place makes it easier to ensure you have appropriate controls in place to protect mission-critical data.
The document discusses Compute Unified Device Architecture (CUDA), which is a parallel computing platform and programming model created by Nvidia that allows software developers to use GPUs for general-purpose processing. It provides an overview of CUDA, including its execution model, implementation details, applications, and advantages/drawbacks. The document also covers CUDA programming, compiling CUDA code, CUDA architectures, and concludes that CUDA has brought significant innovations to high performance computing.
This document provides an overview of CUDA (Compute Unified Device Architecture) and GPU programming. It begins with definitions of CUDA and GPU hardware architecture. The history of GPU development from basic graphics cards to modern programmable GPUs is discussed. The document then covers the CUDA programming model including the device model with multiprocessors and threads, and the execution model with grids, blocks and threads. It includes a code example to calculate squares on the GPU. Performance results are shown for different GPUs on a radix sort algorithm. The document concludes that GPU computing is powerful and will continue growing in importance for applications.
CUDA by Example : Getting Started : NotesSubhajit Sahu
Highlighted notes of:
Chapter 2: Getting Started
Book:
CUDA by Example
An Introduction to General Purpose GPU Computing
Authors:
Jason Sanders
Edward Kandrot
“This book is required reading for anyone working with accelerator-based computing systems.”
–From the Foreword by Jack Dongarra, University of Tennessee and Oak Ridge National Laboratory
CUDA is a computing architecture designed to facilitate the development of parallel programs. In conjunction with a comprehensive software platform, the CUDA Architecture enables programmers to draw on the immense power of graphics processing units (GPUs) when building high-performance applications. GPUs, of course, have long been available for demanding graphics and game applications. CUDA now brings this valuable resource to programmers working on applications in other domains, including science, engineering, and finance. No knowledge of graphics programming is required–just the ability to program in a modestly extended version of C.
CUDA by Example, written by two senior members of the CUDA software platform team, shows programmers how to employ this new technology. The authors introduce each area of CUDA development through working examples. After a concise introduction to the CUDA platform and architecture, as well as a quick-start guide to CUDA C, the book details the techniques and trade-offs associated with each key CUDA feature. You’ll discover when to use each CUDA C extension and how to write CUDA software that delivers truly outstanding performance.
Table of Contents
Why CUDA? Why Now?
Getting Started
Introduction to CUDA C
Parallel Programming in CUDA C
Thread Cooperation
Constant Memory and Events
Texture Memory
Graphics Interoperability
Atomics
Streams
CUDA C on Multiple GPUs
The Final Countdown
All the CUDA software tools you’ll need are freely available for download from NVIDIA.
Jason Sanders is a senior software engineer in NVIDIA’s CUDA Platform Group, helped develop early releases of CUDA system software and contributed to the OpenCL 1.0 Specification, an industry standard for heterogeneous computing. He has held positions at ATI Technologies, Apple, and Novell.
Edward Kandrot is a senior software engineer on NVIDIA’s CUDA Algorithms team, has more than twenty years of industry experience optimizing code performance for firms including Adobe, Microsoft, Google, and Autodesk.
This is a presentation that looks ta some of the Linux commands you could use to identify the hardware on your system. This can be useful for troubleshooting, or just for figuring out which motherboard is in which box.
Using GPUs to handle Big Data with Java by Adam Roberts.J On The Beach
Modern graphics processing units (GPUs) are efficient general-purpose stream processors. Learn how Java can exploit the power of GPUs to optimize high-performance enterprise and technical computing applications such as big data and analytics workloads. This presentation covers principles and considerations for GPU programming from Java and looks at the software stack and developer tools available. It also presents a demo showing GPU acceleration and discusses what is coming in the future.
Graphics processing unit or GPU (also occasionally called visual processing unit or VPU) is a specialized microprocessor that offloads and accelerates graphics rendering from the central (micro) processor. Modern GPUs are very efficient at manipulating computer graphics, and their highly parallel structure makes them more effective than general-purpose CPUs for a range of complex algorithms. In CPU, only a fraction of the chip does computations where as the GPU devotes more transistors to data processing.
GPGPU is a programming methodology based on modifying algorithms to run on existing GPU hardware for increased performance. Unfortunately, GPGPU programming is significantly more complex than traditional programming for several reasons.
The document discusses cache memory, CPU addressing modes, comparing processors, and GPUs. It provides information on cache memory levels L1, L2, and L3 and their characteristics. It describes different addressing modes used by CPUs like direct, indirect, indexed, and relative addressing. When comparing processors, the document advises checking clock speed, core performance, cache size, and benchmark results. It also outlines the differences between integrated and discrete GPUs and their uses for basic versus demanding graphics tasks.
A presentation for all the IT resellers and retailers in Nepal.
Introducing next generation technologies into the consumer market to collectively deliver a greater and richer computer experience.
Hardware refers to all of the physical parts of a computer system. F.pdfanjaniar7gallery
The document provides information about various hardware components of a computer system, including RAM, hard drives, graphics systems, and installing/upgrading these components. RAM is temporary memory that improves performance when more is installed. Hard drives store long-term data, and upgrading to a larger or solid state drive can speed up a computer. Graphics systems handle visual output, and a dedicated graphics card provides better performance for gaming and video editing than integrated graphics. Installing or upgrading RAM, graphics cards, and hard drives involves opening the computer case, inserting/connecting the new component, and ensuring proper installation through software.
Hardware for deep learning includes CPUs, GPUs, FPGAs, and ASICs. CPUs are general purpose but support deep learning through instructions like AVX-512 and libraries. GPUs like NVIDIA and AMD models are commonly used due to high parallelism and memory bandwidth. FPGAs offer high efficiency but require specialized programming. ASICs like Google's TPU are customized for deep learning and provide high performance but limited flexibility. Emerging hardware aims to improve efficiency and better match neural network computations.
At the event was discussed what the developer can use to repair an application or a game if it has graphic display problems. Also, speakers gave an overview of the Mesa library and its development process.
This presentation by Vadym Shovkoplias and Andrew Khulap (Senior Software Engineers, Consultants, GlobalLogic), was delivered at GlobalLogic Kharkiv Embedded TechTalk #2 on June 4, 2018.
Video: https://youtu.be/pT1Y81KGHkM
GPGPU in Commercial Software: Lessons From Three Cycles of the Adobe Creative...Kevin Goldsmith
This was a talk I gave at NVidia's Graphics Technology Conference in San Jose, California in 2010. On NVidia's site you can find this talk, synced with the audio here: http://nvidia.fullviewmedia.com/gtc2010/0923-k-2051.html
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/07/a-new-open-standards-based-open-source-programming-model-for-all-accelerators-a-presentation-from-codeplay-software/
Charles Macfarlane, Chief Business Officer at Codeplay Software, presents the “New, Open-standards-based, Open-source Programming Model for All Accelerators” tutorial at the May 2023 Embedded Vision Summit.
As demand for AI grows, developers are attempting to squeeze more and more performance from accelerators. Ideally, developers would choose the accelerators best suited to their applications. Unfortunately, today many developers are locked into limited hardware choices because they use proprietary programming models like NVIDIA’s CUDA. The oneAPI project was launched to create an open specification and open-source software that enables developers to write software using standard C++ code and deploy to GPUs from multiple vendors.
OneAPI is an open-source ecosystem based on the Khronos open-standard SYCL with libraries for enabling AI and HPC applications. OneAPI-enabled software is currently deployed on numerous supercomputers, with plans to extend into other market segments. OneAPI is evolving rapidly and the whole community of hardware and software developers is invited to contribute. In this presentation, Macfarlane introduces how oneAPI enables developers to write multi-target software and highlights opportunities for developers to contribute to making oneAPI available for all accelerators.
The document discusses graphics processing units (GPUs) and general-purpose GPU (GPGPU) computing. It explains that GPUs were originally designed for computer graphics but can now be used for general computations through GPGPU. The document outlines CUDA and MPI frameworks for programming GPGPU applications and discusses how GPGPU provides highly parallel processing that is much faster than traditional CPUs. Example applications mentioned include molecular dynamics, bioinformatics, and high performance computing.
This document provides system information for a Dell PowerEdge R530 server running Windows Server 2012 R2 Standard. It details the machine name, operating system, processor, memory, storage, audio, network, and other hardware devices. No issues were found with the display, input devices, or USB connectivity. The system has no sound card installed.
The Raspberry Pi is an inexpensive ($35), credit card sized computer that is able to run the Linux operating system. The card also contains USB ports, an Ethernet port, camera port, GPIO lines, serial ports, SPI port, HDMI port, and I2C port – just about anything you would want for an inexpensive and very powerful robot controller! Lloyd Moore will show us how to get started with this device. Specifically we'll talk about loading and configuring the operating system, installing the Qt (C++) development system, and controlling some of the ports.
Multi-faceted Microarchitecture Level Reliability Characterization for NVIDIA...Stefano Di Carlo
These slides have been presented by Dr. Alessandro Vallero at the IEEE VLSI Test Symposium, San Francisco, CA, USA (April 22-25, 2018).
General Purpose computing on Graphics Processing Unit offers a remarkable speedup for data parallel workloads, leveraging GPUs computational power. However, differently from graphic computing, it requires highly reliable operation in most of application domains.
This presentation talk about a “Multi-faceted Microarchitecture Level Reliability Characterization for NVIDIA and AMD GPUs“. The work is the outcome of a collaboration between the TestGroup of Politecnico di Torino (http://www.testgroup.polito.it) and the Computer Architecture Lab of the University of Athens (dscal.di.uoa.gr) started under the FP7 Clereco Project (http://www.clereco.eu). It presents an extended study based on a consolidated workflow for the evaluation of the reliability in correlation with the performance of four GPU architectures and corresponding chips: AMD Southern Islands and NVIDIA G80/GT200/Fermi. We obtained reliability measurements (AVF and FIT) employing both fault injection and ACE-analysis based on microarchitecture-level simulators. Apart from the reliability-only and performance-only measurements, we propose combined metrics for performance and reliability (to quantify instruction throughput or task execution throughput between failures) that assist comparisons for the same application among GPU chips of different ISAs and vendors, as well as among benchmarks on the same GPU chip.
Watch the presentation at: https://youtu.be/GV5xRDgfCw4
Paper Information:
Alessandro Vallero§ , Sotiris Tselonis, Dimitris Gizopoulos* and Stefano Di Carlo§, “Multi-faceted Microarchitecture Level Reliability Characterization for NVIDIA and AMD GPUs”, IEEE VLSI Test Symposium 2018 (VTS 2018), San Francisco, CA (USA), April 22-25, 2018.
∗Politecnico di Torino, Italy. Email: stefano.dicarlo,alessandro.vallero@polito.it †University of Athens, Greece Email: dgizop@di.uoa.gr
This document provides instructions for running AMD accelerated parallel processing applications remotely by allowing the application access to the display driver and GPU. It describes setting up alternative remote desktop utilities for Windows and modifying security settings to allow remote X server access on Linux systems. The solution is to configure VNC on Windows and modify configuration files to allow xhost access and change file permissions on Linux.
infoShare AI Roadshow 2018 - Tomasz Kopacz (Microsoft) - jakie możliwości daj...Infoshare
Podczas tej sesji przyjrzymy się, w jaki sposób można skorzystać z platformy Microsoft do budowy tzw. „inteligentnych” rozwiązań. W przykładach zobaczymy zarówno Cognitive Services, jak i wykorzystaniu GPU (a dokładniej – Batch AI) do uczenia sieci neuronowych. Zajmiemy się także skomplikowanym zagadnieniami związanymi z projektowaniem – tak by algorytmy rozszerzały ludzkie możliwości (a nie nas zastępowały). Sesja zakłada że słuchacze umieją programować.
This technical presentation discusses HTML gaming frameworks for building browser-based 3D games. It provides insights into several frameworks: Construct 2 is a game maker that does not require JavaScript coding; ImpactJS is a tested HTML5 engine that supports multiple platforms; EaselJS and Phaser are frameworks that offer display lists and mouse interactions; Three.js and Voxel.js are used for 3D games; and PlayCanvas focuses on real-time collaboration. The presentation also covers the game loop, which controls the core update and draw functions, and highlights differences in developing 2D versus 3D games. Benefits of HTML games include cross-platform support and using open standards, while challenges relate to varying user experiences across devices and accessing
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.
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
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.
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
How to Create a More Engaging and Human Online Learning Experience
Cuda cracking
1. VERSION 1.0
MARCH 30, 2013
CUDA CRACKING
PRESENTED BY: ROHIT SHAW
XIARCH SOLUTIONS PVT LTD
NEW DELHI
Rohit Shaw Page 1
2. CUDA Cracking
Compute Unified Device Architecture (CUDA) is a parallel computing architecture developed
by Nvidia for graphics processing. CUDA is the computing engine in Nvidia graphics processing
units (GPUs) that is accessible to software developers through variants of industry standard
programming languages.
Introduction: Cuda cracking means cracking passwords with the help of Graphics card which
have GPU, it means the speed of password cracking is much faster than CPU speed.
Building a CUDA Machine: For building a monster cuda machine we have to invest a
huge amount on it. First we have to select a motherboard which supports more than one GPU
because the more GPU means the process of password cracking is much faster. I suggest MSI
Big Bang Marshall Motherboard which supports multiple GPUs up to 8 graphic cards. Another
unique feature of this motherboard that it is cross platform GPU supportable it means it can
support both ATI and Nvidia graphic cards at a time. Use Quad core processors or Intel’s I
family processors for better performance. RAM up to 16 GB is efficient for this motherboard.
Another important thing to keep in mind that is the power supply system we have to supply up to
1250 watt power to this machine. Also use cooling fans as much as possible because during
process graphic cards heats very intensively.
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3. Graphic Card Selection: Graphic card selection is the core important thing before
assembling a cuda machine. Before investing in graphic card first decides which graphic cards
has much cuda cores. Graphics card runs on cuda core it means the number of core is high then
the password cracking performance is also high. Also keep in mind which motherboard is you
are using because all graphic cards did not compatible with all motherboards. We can see the list
of GPU estimations and map the performance of card according to your budget.
We can see the list above here you see that SP/ALU count column it refers the no. of cuda cores
and also see the cracking speed of hashes like MD5, SHA1, WPA and more.
Here we are using Radeon HD 5970 and it has 3200 cuda cores that why the cracking speed is
much pretty good.
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4. Now after card selection you have to select a proper GPU supported tool from which you will
able to crack hash. There are many tools which supports GPU processor. Tools are based on
operating systems some tools are Windows Supported and some of them are Linux supported.
We are going to now describe some tools according to operating system compatibility.
Windows Supported Tools:
IGHASHGPU: This tool is developed by Ivan Golubev. This tool can crack only three hashes
SHA1, MD5 and MD4. It is compatible with ATI and Nvidia cards. The ATI cards which
supports are Radeon HD 4550, 4670, 4830, 4730, 4770, 4850, 4870, 4890, 5750, 5770, 5850,
5870, 5970 and Nvidia cards with CUDA support.
BarsWF: BarsWF is developed by Svarichevsky Mikhail. It supports on Nvidia cards and also
known as world’s fastest MD5 cracker. Hash supports only MD5.
Extreme GPU Bruteforcer: It is a commercial tool developed by InsidePro. It supports total 58
types of hashes MD5, MD4, NTLM, SHA-1, SHA-512 and many more. Utilizing the power of
multiple graphics cards running simultaneously (supports up to 32 GPU), the software allows
reaching incredible search speeds of billions of passwords per second. It supports only Nvidia
cards.
Lightning Hash Cracker: Lightning Hash Cracker is developed by Elcomsoft and it is a freeware
tool. It supports only MD5 hashes and Nvidia compatible.
Oclhashcat Plus: It is a popular hash cracker tool and it supports ATI and Nvidia cards
simultaneously. It works on Windows and Linux based operating systems. Multi GPU supports
up to 128 cards. It supports 57 types of hashes.
Cryptohaze Multiforcer: It is an open source tool. This is only a tool which can used on network
based password cracking so multiple systems can work on the same. It supports 17 types of
hashes MD5, MD4, NTLM, LM and more. It supports only Nvidia cards are 8000 series, 9000
series, GTX200 series, GTX400/500 series.
LinuxSupported Tools:
New Multiforcer: New Multiforcer is the new version for Cryptohaze Multiforcer and an open
source tool which supports ATI and Nvidia cards. The older version means Cryptohaze
Multiforcer does not supports ATI cards. New Multiforcer supports only 9 types of hashes.
Oclhashcat Plus: It is a muti platform working tools runs on Windows and Linux based operating
systems. It also supports a large number of hashes and GPU enabled cards up to 128 GPU cards.
Works on both Cards ATI and Nvdia.
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5. Whitepixel: Whitepixel is an open source tool supports only MD5 hash and runs only on ATI
cards. The ATI cards which supported are AMD Radeon HD 5000 series and above series. It is
also a Multi GPU supportable program upto 8 GPU cards.
Hashkill: Hashkill is an open source tool. It works on both AMD and Nvidia cards. It has 40
plugins for different type of passwords ranging from simple hashes MD5, SHA1 to private SSL
key passphrases.
We can see now the description of all tools which are GPU supported but we can’t identify that
which tool is good and fast for hash cracking. So we will now test some of the tools on both
Operating Systems (Windows & Linux) by cracking a MD5 hash and point out the cracking
speed.
Windows CUDA Machine: For configuring cuda machine on Windows operating systems
we have to install first ATI drivers the R.G catalyst according to your graphic card model.
Tools Demonstration on Windows CUDA Machine: We are going to use
IGHASHGPU. It is very simple command line tool now we will the usage command of
IGHASHGPU.
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6. By executing ighashgpu.exe from command line it will show the all command options with
detail some of the syntax we will describe here which is necessary.
-c: for character sets defining (caps, small, digits, special, space, all)
-h: hash value
-t: type of hash (MD5, MD4 or SHA1)
-min: minimum password length
-max: maximum password length
Now let us test our CUDA machine. We require a MD5 hash of a password let make it. Go to
online md5 hash generator service like here we are using www.md5.cz and as password we are
giving Xi4rCh and generate a MD5 hash of it.
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7. Now we can continue to further cracking process. Run ighashgpuu.exe and type in these
commands
ighashgpu.exe /h:a52a81807a28e5f92893dd5106c9ce65 /t:md5 /c:csda /max:7 /cpudontcare
We already know about the syntax usage so we will not describe the syntax function here. The
cracking process starts
Now in above figure we can see average password cracking speed is 1116.8 million per second
and estimated time is showing approximately 11 min. But we in our case the password found in 5
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8. minutes which we can see in below figure. The cracking speed is increases to 1119.1 million.
Now we can see here that alphanumeric password (Uppercase, Lowercase, Digits) within 6
character can be cracked in 5 minutes.
Let us try that if we crack the same MD5 hash with CPU power then what is the efficiency? So
we are using here Cain n Able for cracking MD5 hash and see what happens.
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9. In the above screenshot we can see the average time it will take to crack 1.32945 years so we can
see the difference between the GPU and CPU efficiency.
BarsWF: It supports only Nvidia card so we are going to crack on CPU power. Execute the
BarsWF.exe from command line and it will show all the command options in details some of the
commands we will demonstrate here which we are going to use.
Usage Syntax:
-c: for character set defining (A-for caps, a-for small, 0-digit, ~-for special characters)
-h: hash here
-min_len: minimum password length
So here we will using the same MD5 hash and type in BarsWF_SSE2_x64.exe –c A0a –h
a52a81807a28e5f92893dd5106c9ce65 –min_len 8
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10. We can see in above figure the estimate time it will take 27 days it means it is faster than Cain n
Abel where it takes a year to crack this hash, BarsWF will take some days.
Linux CUDA Machine: Configuring CUDA machine in Linux system is not easy as
Windows. The driver installation process is different in ATI and Nvidia cards.
ATI Driver installation:
1. Remove the old AMD drivers
sudo sh /usr/share/ati/fglrx-uninstall.sh
sudo apt-get remove --purge fglrx fglrx_* fglrx-amdcccle* fglrx-dev* xorg-driver-fglrx
2. Download the AMD drivers
Download from AMD website
Or via terminal
cd ~/; mkdir catalyst12.4; cd catalyst12.4/
wget -O amd-driver-installer-12-4-x86.x86_64.run http://goo.gl/VGYWP
3. Installing Drivers
chmod +x amd-driver-installer-12-4-x86.x86_64.run
sudo sh ./amd-driver-installer-12-4-x86.x86_64.run
Now continue the installation wizard.
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11. Nvidia Driver Installation:
1. Edit the configuration files
gedit /etc/modprobe.d/blacklist.conf
add these line in blacklist.conf file
blacklist vga16fb
blacklist nouveau
blacklist rivafb
blacklist nvidiafb
blacklist rivatv
2. apt-get --purge remove nvidia-*
3. Reboot the system.
4. Installing Drivers
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12. add-apt-repository ppa:ubuntu-x-swat/x-updates
apt-get update && apt-get install nvidia-current nvidia-current-modaliases nvidia-settings
5. Again reboot and you will Nvidia option in your utilities.
Tools Demonstration on Linux CUDA Machine:
In Linux operating system we are going to use Nvidia cards with has less cuda cores than ATI
Radeon card.
Cryptohaze Linux: Now let’s see the usage command for Cryptohaze. Type in this command
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13. ./Cryptohaze-Multiforcer –help
We have to use only three commands for cracking a hash, which is:
-c (For charset files)
-h (For defining hash type)
-f (For hash file location)
Charset: The term charset is short form of character set. It is a defined list of characters
recognized by the computer hardware and software. Like you create a text document and define
characters in lower case a-z and save it. Now you can create many charset file by defining own
characters 0-9, A-Z and more like shown in below Figure.
Now let’s start cracking. Type in:
./Cryptohaze-Multiforcer –c /root/Desktop/Cryptohaze-Linux/charsets/charsetall –h MD5 –
f /root/Desktop/hash.txt
Then press enter and cracking process starts.
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14. See in top right corner side there shows my GPU cracking speed is only 36.64M/s it’s very slow
because my Nvidia card has only 16 cuda cores. After successfully cracking it shows the
password from your hash which is p@ssw
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15. New Multiforcer: New Multiforcer is new revision version of Cryptohaze Multiforcer which
supports ATI cards. The usage command is same as Cryptohaze Multiforcer. We have to pass
these commands –openclplatform=0, --opencldevice=1 –bfi_init
./NewMultiforcer --openclplatform=0 --opencldevice=1 –bfi_init -h Hash type -c my
charsets dir -f my file dir
Which is better ATI or Nvidia?
Now which card is better it depends on many factors like price, tools and Cuda cores. If we are
matching the speed of password cracking then we see ATI cards are better than Nvidia because
the number of cuda cores is greater than Nvidia cards. Nvidia cards are expensive than ATI
cards. But nvidia provides a high performance cards like Tesla cards and Titan cards which are
very expensive but very fast performance. Maximum tools are Nvidia compatible on both
platforms Windows and Linux. We found that very few tools are support ATI cards. We found
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16. that if you don’t care of money then definitely go through with Nvidia cards but if you have
small budget then go for ATI cards.
Conclusions: Hence we conclude that if we used a high performance graphic card with high
cores which is greater than 512 cores and also use more than one card than the cracking speed of
our machine is much faster than this machine, here we see the efficiency of both ATI and Nvidia
cards. When we are using ATI card which has 3200 cuda cores it gives a speed of 1116.8
Million/second and when we were using only Nvidia card which has only 16 cuda cores it gives
only 36 Million/second. Now you can understand the difference and importance of Cuda cores.
References:
http://en.wikipedia.org/wiki/CUDA
http://golubev.com/gpuest.htm
http://cyruslab.wordpress.com/2012/01/26/installing-nvidia-on-
backtrack5r1/
About Me:
Rohit Shaw is a Certified Ethical Hacker works as a penetration tester with Xiarch AAG Group. He
has experience in pentesting, social engineering, password cracking and malware obfuscation.
Rohit Shaw Page 16