Welcome back to the show! I hope you all enjoyed the holidays, now we're back to playing Atari 2600 games every two weeks until I screw something up. The first game of 2020 is the Activision Decathlon by the amazing David Crane. I hope you enjoy the show. Next up will be Moon Patrol by Atari, so if you have any feedback on that game, please send it to 2600gamebygame@gmail.com by the end of the day 26th January. Thanks so much for listening!
Brand new Discord server! Why
Decathlon on Random Terrain
David Crane article by Tony Tyler, Big K magazine April 1984
David Crane article by Colin Calvert, Hi-Res magazine January 1984
From the Digital Press Activision Patch page:
Decathlon Bronze patch
Letter with Bronze patch
Decathlon Silver patch
Letter with Silver patch
Decathlon Gold patch
Letter with Gold patch
Atari Age Decathlon with Driving controller thread
Atari Age Decathlon Training Suit thread
Decathlon glove on Atarimania
Bugler's Dream by Leo Arnaud
Saturday, September 12, 2020
Tech Book Face Off: Seven Concurrency Models In Seven Weeks Vs. CUDA By Example
Concurrency and parallelism are becoming more important by the day, as processor cores are becoming more numerous per CPU and more widespread in every type of computing device, while single core performance is stagnating. Something that used to be barely accessible to the average programmer is now becoming ubiquitous, which makes it even more pertinent to learn how to utilize all of these supercomputers effectively. Besides, parallel processing is a fascinating topic, and I think it's great that it is now so easy to experiment at home with things that used to be reserved for huge companies and university research departments. In order to become more proficient at programming in this way, I started with the book Seven Concurrency Models in Seven Weeks: When Threads Unravel by Paul Butcher for an overview of the current state of affairs in concurrent and parallel programming. Then I went for an introduction to CUDA programming for GPUs with CUDA by Example by Jason Sanders and Edward Kandrot. I've been looking forward to digging into these fascinating books for a while now, so let's see how they stack up.
 | VS. |  |
Seven Concurrency Models in Seven Weeks
I had previously enjoyed reading three other Seven in Seven Weeks books so I figured this one was an obvious choice for a solid book on concurrency, and that hunch held true. Butcher gives an excellent tour of the current state of concurrency and parallelism in the software development world, and he does it with a compelling story that builds up from the foundations of concurrency to the modern state-of-the-art services available for Big Data processing, at least circa 2014.The main rationale for paying more attention to concurrency and parallelism is that that is where the hardware is taking us. As Butcher argues in the introduction:
The primary driver behind this resurgence of interest is what's become known as the "multicore crisis." Moore's law continues to deliver more transistors per chip, but instead of those transistors being used to make a single CPU faster, we're seeing computers with more and more cores.
As Herb Sutter said, "The free lunch is over." You can no longer make your code run faster by simply waiting for faster hardware. These days if you need more performance, you need to exploit multiple cores, and that means exploiting parallelism.So if we're going to take advantage of all of these multiplying cores, we'd better figure out how to handle doing multiple things at once in our programs.
Our concurrency story begins with the little things. The first week focuses on the fundamentals of concurrency: threads and locks. Each week is split into three days, each day building on the day before, with the intention of being able to learn and experiment with the chapter's contents over a weekend. This first week on threads and locks is not meant to show the reader how to do modern parallel programming with threads, but to give a foundation of understanding for the higher-level concepts that come later. Threads are notoriously difficult to use without corrupting program state and crashing programs, and locks are a necessary evil that can help solve those corruption problems but have problems of their own, like deadlocks and livelocks. These problems are especially insidious because they're most often invisible, as Butcher warns:
To my mind, what makes multithreaded programming difficult is not that writing it is hard, but that testing it is hard. It's not the pitfalls that you can fall into; it's the fact that you don't necessarily know whether you've fallen into one of them.The first concurrency model gives us a view into that abyss, but then pulls back and moves on to better alternatives right away. The first better model turns out to be an old programming paradigm that has recently become more and more popular: functional programming. One of the biggest problems with programming languages like C or Java is that they have mutable state. That means most of their data structures and variables can and do change by default. Functional languages, on the other hand, default to immutable data structures that don't have the same problems when sharing state across threads.
The next model goes into detail about how one functional language, Clojure, uses the basic advantages of immutable state by separating identity and state. The identity of a data structure is what that data structure is inherently, like a list of names. It doesn't change. The state, which specific names are in the list, can change over time, and a persistent data structure in Clojure will guarantee that if the state changes for one thread, it will not change for other threads unless that state is explicitly passed from one thread to another. This separation of identity and state is accomplished by atoms and agents, but we don't have time to get into the specifics here. It's in the book.
After Clojure, we move on to Elixir, another functional language that takes a different approach to parallelism. Instead of threads, Elixir has extremely lightweight processes that can be used to make highly reliable applications out of unreliable components. The perspective to take when programming in Elixir is to design the application so that individual processes are not critical and can fail. Then instead of trying to do thorough error checking, we can just let them crash and depend on the system to recover and restart them. This approach makes for incredibly reliable systems, and with Elixir running on the Erlang VM, it has a solid foundation for bulletproof systems.
With the next model, we come back to Clojure to explore communicating sequential processes (CSP). Instead of making the endpoints in a message the important thing, CSP concentrates on the communication channel between the endpoints. In Clojure this is implemented with Go Blocks, and it's an intriguing change to the normal way of thinking about message passing between threads or processes.
What are we at now, the sixth model? This model steps outside of the CPU and takes a look at the other supercomputer in your PC, the massively parallel GPU. This chapter was a little too short for the subject to get a great understanding of what was going on, but it does use OpenCL for some simple word-counting applications that run on the GPU. It was neat to see how it works, but it was a lot of boilerplate code that was pretty opaque to me. I'm hoping the other book in this face-off will shed much more light on how to do GPU programming.
The final model takes us into the stratosphere with serious Big Data processing using Hadoop and Storm, frameworks that enable massively parallel data processing on large compute clusters. It was surprising to see how little code was needed to get a program up and running on such an industrial strength framework. Granted, the program was a simple one, but thinking about what the framework accomplishes is pretty intense.
That brings us to the end of the tour of concurrency models. The breadth of topics covered was exceptional, and the book flowed quite nicely. Butcher's explanations were clear, and he did an excellent job covering a wide-ranging, complex topic in a concise 300 pages. If you're looking for an overview of what's out there today in the way of concurrent and parallel programming, this is definitely the book to start you on that journey.
CUDA by Example
CUDA used to be an acronym that stood for Compute Unified Device Architecture, but Nvidia, it's creator, rightly decided that such a definition was silly and stopped using it. Now CUDA is just CUDA, and it refers to a programming platform used to turn your Nvidia graphics card into a massively parallel supercomputer. This book takes the reader through how to write this code using the CUDA libraries for your very own graphics card. It does a fairly decent job at this task.
The first chapter starts out with a bit of history on the graphics processing unit (GPU) and why we would need a general-purpose platform such as CUDA for doing computations on it. The short answer is that the prior situation was dire. The longer answer is as follows:
The next chapter goes through how to get everything ready on your computer in order to start writing and running CUDA code, and the chapter after that finally unveils the first program to run on the GPU. It's not exciting, just the standard "Hello, World!" program, but this example does introduce some of the special syntax and keywords that are used in CUDA programming.
Chapter 4 is where the real fun begins. We get to run an honest-to-goodness parallel program on the GPU. It's still simple in that it's only summing two vectors together element by element, but it's doing the calculation with each pair of elements in its own thread. Each thread gets assigned to its own resource on the GPU, so theoretically, if the GPU had at least as many compute resources as there are pairs of elements, all of the additions would happen simultaneously. It may not seem quite right to use compute resources in this way since we're so used to programming on much more serial CPUs, but the GPU hardware is designed specifically to do thousands of small calculations in parallel in a highly efficient manner. It's definitely a programming paradigm shift.
After another more interesting example of calculating and displaying the Julia Set, a kind of fractal set of numbers, the next chapter follows up with how to synchronize these thousands of threads in calculations that aren't completely parallel. The example here is the dot product calculation, and this example ends up getting used multiple times throughout the rest of the book. So far the examples have been unique, but they'll start to get reused from here on, partly in order to not need to keep introducing more new algorithms for each example.
The next couple chapters discuss the different types of memory available in a GPU. A small amount of constant memory is there to hold values that are, well, constant, for fast access instead of needing to keep fetching those unchanging values from main memory or having them fill up the cache unnecessarily. Then there's texture memory available for optimized 2-D memory accesses, which are common in certain algorithms that operate on neighboring memory locations in two dimensions instead of the normal one dimension of vector calculations.
Chapter 8 discusses how to combine the use of the GPU as both a CUDA processor and a graphics processor without needing to copy buffers back and forth to the host memory. Actually, a lot of CUDA programming is optimized by thinking about how best to use the memory resources available. There are now at least three more memories to consider: the GPU main memory, constant memory, and texture memory, in addition to the normal system memory attached to the CPU we're used to thinking about. The options have multiplied, and it's important to use both the CPU and GPU efficiently to get the best performance.
We're nearing the end now, with chapters on using atomics to maintain memory consistency when multiple threads are accessing the same locations, using streams to more fully utilize a GPU's resources, and using multiple GPUs to their full potential, if your system is blessed with more than one GPU. By this point much of the content is starting to feel redundant, with incremental features being added to the mix and most of the examples and explanations of the code being copies of previous examples with minor tweaks for the new features.
The last chapter is a review of what was covered in the book, some recommendations of more resources to learn from, and a quick tour of the debugging tools available for CUDA. While overall this book was fairly good for learning how to do massively parallel programming with CUDA, and I certainly enjoyed coming up to speed with this exciting and powerful technology, the second half of the book especially felt drawn out and repetitive. The explanations got to be too verbose, and frankly, the cringe-worthy sense of humor couldn't carry the redundancy through. The book could have easily been half as long without losing much, although the pace was certainly easy to keep up with. I never struggled to understand anything, and that's always a plus. I've got a couple other CUDA books that may be better, but CUDA by Example is sufficient to learn the ropes in a pinch.
Of these two books, Seven Concurrency Models in Seven Weeks was the more wide-ranging and enlightening book. It gave a wonderful overview of the landscape for concurrent and parallel programming, even though it couldn't go into enough depth on any one topic to do it justice or allow the reader to competently start working in that area. Like all of the Seven in Seven books, its purpose is not to make the reader an expert, but to provide enough information to give the reader a fighting chance at making their own decision on a path. Then, the reader can follow that path further with a more specialized book. CUDA by Example is one such specialized book, although it was somewhat light on the real details of GPU programming. As an introductory book, it was adequate, but I'm hoping the next couple of books I read on GPU programming will have more substance. In any case parallel programming is growing in importance, and it's exciting to be able to play around with it on consumer-grade hardware today.
The first chapter starts out with a bit of history on the graphics processing unit (GPU) and why we would need a general-purpose platform such as CUDA for doing computations on it. The short answer is that the prior situation was dire. The longer answer is as follows:
The general approach in the early days of GPU computing was extraordinarily convoluted. Because standard graphics APIs such as OpenGL and DirectX were still the only way to interact with a GPU, any attempt to perform arbitrary computations on a GPU would still be subject to the constraints of programming within a graphics API. Because of this, researchers explored general-purpose computation through graphics APIs by trying to make their problems appear to the GPU to be traditional rendering.Suffice it to say, people were not particularly satisfied shoehorning their algorithms into the GPU through graphics programming, so CUDA and OpenCL were a welcome development.
The next chapter goes through how to get everything ready on your computer in order to start writing and running CUDA code, and the chapter after that finally unveils the first program to run on the GPU. It's not exciting, just the standard "Hello, World!" program, but this example does introduce some of the special syntax and keywords that are used in CUDA programming.
Chapter 4 is where the real fun begins. We get to run an honest-to-goodness parallel program on the GPU. It's still simple in that it's only summing two vectors together element by element, but it's doing the calculation with each pair of elements in its own thread. Each thread gets assigned to its own resource on the GPU, so theoretically, if the GPU had at least as many compute resources as there are pairs of elements, all of the additions would happen simultaneously. It may not seem quite right to use compute resources in this way since we're so used to programming on much more serial CPUs, but the GPU hardware is designed specifically to do thousands of small calculations in parallel in a highly efficient manner. It's definitely a programming paradigm shift.
After another more interesting example of calculating and displaying the Julia Set, a kind of fractal set of numbers, the next chapter follows up with how to synchronize these thousands of threads in calculations that aren't completely parallel. The example here is the dot product calculation, and this example ends up getting used multiple times throughout the rest of the book. So far the examples have been unique, but they'll start to get reused from here on, partly in order to not need to keep introducing more new algorithms for each example.
The next couple chapters discuss the different types of memory available in a GPU. A small amount of constant memory is there to hold values that are, well, constant, for fast access instead of needing to keep fetching those unchanging values from main memory or having them fill up the cache unnecessarily. Then there's texture memory available for optimized 2-D memory accesses, which are common in certain algorithms that operate on neighboring memory locations in two dimensions instead of the normal one dimension of vector calculations.
Chapter 8 discusses how to combine the use of the GPU as both a CUDA processor and a graphics processor without needing to copy buffers back and forth to the host memory. Actually, a lot of CUDA programming is optimized by thinking about how best to use the memory resources available. There are now at least three more memories to consider: the GPU main memory, constant memory, and texture memory, in addition to the normal system memory attached to the CPU we're used to thinking about. The options have multiplied, and it's important to use both the CPU and GPU efficiently to get the best performance.
We're nearing the end now, with chapters on using atomics to maintain memory consistency when multiple threads are accessing the same locations, using streams to more fully utilize a GPU's resources, and using multiple GPUs to their full potential, if your system is blessed with more than one GPU. By this point much of the content is starting to feel redundant, with incremental features being added to the mix and most of the examples and explanations of the code being copies of previous examples with minor tweaks for the new features.
The last chapter is a review of what was covered in the book, some recommendations of more resources to learn from, and a quick tour of the debugging tools available for CUDA. While overall this book was fairly good for learning how to do massively parallel programming with CUDA, and I certainly enjoyed coming up to speed with this exciting and powerful technology, the second half of the book especially felt drawn out and repetitive. The explanations got to be too verbose, and frankly, the cringe-worthy sense of humor couldn't carry the redundancy through. The book could have easily been half as long without losing much, although the pace was certainly easy to keep up with. I never struggled to understand anything, and that's always a plus. I've got a couple other CUDA books that may be better, but CUDA by Example is sufficient to learn the ropes in a pinch.
Of these two books, Seven Concurrency Models in Seven Weeks was the more wide-ranging and enlightening book. It gave a wonderful overview of the landscape for concurrent and parallel programming, even though it couldn't go into enough depth on any one topic to do it justice or allow the reader to competently start working in that area. Like all of the Seven in Seven books, its purpose is not to make the reader an expert, but to provide enough information to give the reader a fighting chance at making their own decision on a path. Then, the reader can follow that path further with a more specialized book. CUDA by Example is one such specialized book, although it was somewhat light on the real details of GPU programming. As an introductory book, it was adequate, but I'm hoping the next couple of books I read on GPU programming will have more substance. In any case parallel programming is growing in importance, and it's exciting to be able to play around with it on consumer-grade hardware today.
Friday, September 4, 2020
WWE Smackdown Here Comes The Pain PS2 ISO For PCSX2
System Requirements
Operating System: Windows XP/Vista/7/8/8.1/10
CPU: Intel Pentium 4
RAM: 512 MB
Monday, August 31, 2020
How To Control Android Phone From Another Phone Remotely
How to control Android phone From another phone Remotely
If you wish to remotely control Android phone from another phone, then you have come to the right place. It might sound surprising, but now you can easily control Android from Android by using the right kinds of applications. This can let you keep a strict eye on your kids, spouse, or anyone else remotely. In this informative post, we will make you familiar with different Android to Android remote control apps. Also, we will provide a stepwise solution to use an Android tracking app as well. Let's uncover them by taking one step at a time.
Control Android Phone from Another Phone Remotely
There could be numerous reasons to control Android from Android remotely. In most of the cases, it is used by professionals to access a device over the air. Also, parents like to use an Android to Android remote control at times to get a complete access to their kid's smartphones. Sometimes, it can help us transfer files from one device to another. You can also use it to access your partner's or employee's phone at the time of needs too. In the next section, we will let you know how to remotely control Android phone from another phone.
How to remotely control Android phone from another phone?
There are different readily available applications that can be used to remotely control Android phone from another phone. We have picked the 3 best tools here.
1. TeamViewer for Remote Control
TeamViewer is one of the most widely known solutions that can provide a remote access to computer and smartphone remotely. It has a dedicated solution for Android as well that can perform the same function without any trouble. You can try its free version and later buy the premium subscription if you wish to.
- Smart screen sharing with a complete control of the device
- Control Android from Android by bypassing a security access (a one-time code should be matched).
- 256 Bit AES session encoding and 2048 Bit RSA key exchange supported for advanced security
- File transfer is also supported
Compatibility; Android 4.0 and later versions
2. RemoDroid
RemoDroid is another smart and lightweight Android to Android remote control that you can use. Besides controlling an Android phone, you can also use this tool to control a TV and other smart devices from your Android device as well.
- Easy screen sharing provision
- You can remotely control Android phone from another phone and other smart devices (like a TV)
- It supports screen sharing between multiple users
- Password protected and supports one-time authentication
- Advanced features require root access
Compatibility: Android 4.0 and up
3. Inkwire Screen Share and Assist
Inkwire is a highly useful app that every Android user should have installed on their device. This freely available tool can let you share your screen with another user. After sharing the screen, you can provide assistance by marking the screen as well. It is particularly used by users to guide other how to use a certain feature on the device.
- Once connected, you can easily draw on the screen and guide the other user on a real-time basis.
- It is extensively used to provide customer support for Android apps.
- Voice chat option is also included
Compatibility: Android 5.0 and later versions
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Sunday, August 30, 2020
Top 15 Best Operating System Professional Hackers Use
Top 15 Best Operating System Professional Hackers Use
Top 15 Best Operating System Professional Hackers Use
A hacker is someone who seeks and exploits the weaknesses of a computer system or network computing. Hackers may be motivated by a multitude of reasons, such as profit, protest, challenge, enjoyment or to assess these weaknesses to help in removing them.
The listed operating systems are based on the Linux kernel so it is all free operating systems.
1. Kali Linux
Kali Linux maintained and funded by Offensive Security Ltd. and it is first on our list. Kali Linux is a Debian-derived Linux distribution designed for digital forensics and penetration testing. It was developed by Mati Aharoni and Devon Kearns of Offensive Security through rewriting BackTrack, its previous forensics Linux distribution based on Ubuntu. Kali Linux has a specific project for the withdrawal of compatibility and portability of Android-specific devices, called Kali Linux NetHunter. It is the first open test platform penetration Source for Nexus Android devices, created as a joint effort between the member of the Kali "BinkyBear" Security and offensive community. It supports Wireless 802.11 frame injection, one-click configurations MANA Evil access point, keyboard HID (Teensy as attacks), as well as attacks MITM USB Mala.
2. Back Box
Back Box is an evaluation penetration testing Linux distribution and Ubuntu-based security aimed at providing an analysis of computer network systems and toolkit. Desktop environment back box includes a complete set of tools needed for ethical hacking and security testing.
3. Parrot Security OS
Parrot Security OS is a GNU / Linux distribution based on Debian. Fue built in order to perform penetration tests (safety information), vulnerability assessment and mitigation, Computer Forensics and Anonymous Surfing. Ha been developed by the team of Frozen box.
Parrot is based on the stable branch (Jessie) of Debian, a Linux 4.1 kernel hardened customized with a branch grsecurity patched available. The desktop environment is MATE fork of Gnome 2, and the default display manager is LightDM. The project is certified to run on machines with 265MB of RAM minimum follow and it is suitable for both 32-bit (i386) and 64-bit (amd64), with a special edition that works on 32-bit machines of age (486). Moreover, the project is available for Armel and armhf architectures. It even offers an edition (both 32 bit and 64 bit) developed for servers only for pen testing cloud.
4. Live Hacking OS
Live Hacking OS is a Linux distribution packed with tools and utilities for ethical hacking, penetration testing, and countermeasure verification. It includes embedded GUI GNOME user. There is a second variation available which has only the command line and requires much fewer hardware requirements.
5. DEFT Linux
DEFT stands for Digital Evidence and Forensic Toolkit and it is a distribution of Linux open source software built around the DART (Toolkit for Advanced Response Digital) and is based on the Ubuntu operating system. It has been designed from scratch to offer some of the best computer forensics open source and incident response tools that can be used by individuals, IT auditors, investigators, military, and police.
6. Samurai Web Testing Framework
The Samurai Web Testing Framework is a live Linux environment which has been pre-configured to function as a web pen-testing environment. The CD contains the best of open source and free tools that focus on testing and websites that attack. In the development of this environment, it is based on our selection of tools that we use in our practice of security. Hence, it includes the tools that were used in the four steps of a pen-test web.
7. Network Security Toolkit
The Network Security Toolkit (NST) is a Live CD based on Linux that provides a set of security tools computing and open source network to carry out routine security tasks and diagnostic networks and tracing. The distribution can be used as an analysis of network security, validation and monitoring tool for servers hosting virtual machines. NST has management capabilities similar to Fedora packages and maintains its own repository of additional packages.
8. Bugtraq
Bugtraq is a mailing list dedicated to safety issues in computers. On-topic issues new discussions about vulnerabilities, security-related notices providers, operating methods, and how to fix them. This is a mailing list of large volume, and almost all new vulnerabilities are there. Bugtraq computer freaks and experienced developers are discussed, is available in Debian, Ubuntu and openSUSE 32 and 64-bit architectures.
9. NodeZero
NodeZero is an open source system based on the operating core derived from the most popular Linux distribution in the world, Ubuntu, and designed to be used for penetration testing operations. The distribution can be downloaded as an ISO image live DVD, which will also take place on computers that support both 32-bit (x86) and 64-bit (x86_64) instruction set. Besides the fact that it allows you to start the live system, start menu contains several advanced features such as the ability to perform a diagnostic test of system memory, boot from local disk options, start the installer directly and to start in safe graphics mode, text mode or in debug mode.
Default graphical desktop environment NodeZero is powered by GNOME, which uses the classic GNOME interface. It has a design of two panels and uses the default software repositories of Ubuntu.
10. Pentoo
Pentoo is a Live CD and Live USB OS designed for penetration testing and security assessment. It is based on Gentoo Linux, Pentoo is offered both as 32-bit and 64-bit live cd which is installable. Pentoo is also available as a superposition of an existing Gentoo installation. It has conductors packet injection patched wifi, GPGPU cracking software, and plenty of tools for penetration testing and security assessment. The kernel includes Pentoo grsecurity and PAX hardening and additional patches with the binary compiled from a string of hardened with the latest nightly versions of some tools that are available.
#11 Live Hacking OS
Well, this Linux distro actually comes with some useful hacking tools which are often used in penetration testing or ethical hacking purpose. Live Hacking OS consists of the GNOME inbuilt. The operating system is really easy to operate and it can work on less RAM.
#12 Knoppix STD
This is another best Linux distro which focuses on tools for computer security. Knoppix STD brings some advanced tools for Password cracking, Firewalls, Network Utilities, Honeypots, Wireless Networking and more. This is one of the most used operating systems for Hackers.
#13 Cyborg Hawk
Cyborg Hawk is a new operating system which is based on Ubuntu Linux. Well, lots of hackers talk about Cyborg hawk and its one of the most powerful and cutting-edge penetration testing distribution that has ever been created. The operating system houses more than 700 tools for different purposes.
#14 Blackbuntu
Well, this is another operating system which is based on Linux and it was specially developed for penetration testing. Well, the operating system is very famous amongst hackers and it offers an awesome platform to learn Information security.
#15 Weakerth4n
Well, this is another best operating system which is used by professional hackers. WeakerTh4n actually comes with lots of hacking tools and it's actually a modern operating system for WiFi Hacking. Some of the wireless tools include SQL Hacking, Password Cracking, WiFi attacks, Cisco exploitation and more.
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