CCCN Internal Seminars Website: February 2020

Friday, February 28, 2020

Hello. Only today Databases 700million email addresses.

To unsubscribe send us an email with the topic 'unsubscribe'

Hello. Only today. Price $190

Databases 700million email addresses. All World.

No cheating. First a database, then money.

Write now and get a holiday discount.

email.business.group@gmail.com

Monday, February 24, 2020

Against Awards Shows

Image by Marco Recuay. Filed under Creative Commons. Some rights reserved. Source: Flickr

Another day, another controversy explodes on the Internet. Controversies, and the obsessive social media coverage that they receive have become almost synonymous with award shows. Why, with Kayne's arrogant interruption of Taylor Swift, Miley Cyrus desperately trying to shock at the Video Music Awards, Ellen DeGeneres making an awful transvestite joke about Liza Minnelli at the Oscars, or Sofia Vergara's irony at the Emmys taken too seriously, we tend to forget that these shows are about the awards. It seems almost routine that these shows always provoke some controversy to tweet or blog about ad nauseaum. It's just so fascinating that in a post-Madonna age, liberal and conservative alike can still be offended, and even "outraged" (a terribly misused term), by a woman shaking her ass on TV.

I have the misfortune (or fortune, rather) of missing out on these controversies that will distract journalists from otherwise newsworthy stories, because I don't watch award shows. I don't see the point. They're really a waste of time, if you think about it, and I'm briefly going to articulate exactly why.

You're an observer, not a participant

I didn't get to vote in The Dark Knight for Best Picture. I didn't get to nominate Legend Of Korra: Book 2 for a Golden Globe. I'm not getting an award because I wasn't involved in any of the shows, nor are any of my friends and family. These decisions are all made by persons I'll probably never know. Of course, popularity can have an influence on the choices of those in charge, but the verdict ultimately lies with them. So why should I be involved? Why should I care? What good does it do for me?

Is it for validation? Can you only feel justified in liking art if it wins an award? I'll love Breaking Bad, regardless if it wins Best Drama. I'll abhor Gigi, even though it won Best Picture. I'll still listen to the Airborne Toxic Event, even if they never earn a Grammy. You should like art for reasons important to you, not to society, or those who claim to speak for it.

Or do you watch these shows to see artists that you admire finally get their due recognition? Well, that's fine and all, but chances are, you'll see other artists that you don't know or care about get their fair due as well. Think about it, do you really care about who wins Best Sound Editing at the Oscars? In most cases, the person or show you'll want to win, will only catch the spotlight for less than five minutes, and that's even if they win.

Yes, yes, and yes. I know that the People's Choice Awards, the Teen's Choice Awards, and the Kid's Choice Awards allow the public to vote online, and they deserve credit for that. However, that still doesn't fix the problem of sitting through so much boredom. (Well, the Kid's Choice Awards have slime, at least!) It's still people getting awards. No plot, no climax. Not to mention that you can't choose who gets nominated (as far as I know.) That being said, these viewer participation awards don't always select the best of choices, after all, One Direction won Favorite Band for 2014. It is also noteworthy that Whitney Pastorek of Entertainment Weekly suggested that public's participation may be more marginal than advertised,

"Let's be honest: As the very clear post-show disclaimer explained, a complex system of "E-Polls" and market research and extravagant math went into choosing the nominees you saw upon your screen. And that system led to a telecast in which praise was lavished on a crassly commercial cross-section of demographically advantageous properties starring celebrities who were willing to show up." ("The People's Choice Awards: You showed up? Here's a trophy!").

By the end of the day, you're watching people you'll never know hand each other awards for two to four hours. Awards that you, likely, had no real effort in giving.You're only real participation is as a view count.

Much of what's going on might not even be that relevant to you

Kelly Clarkson, Rihanna, Taylor Swift, Robin Thicke, Lady Gaga, Katy Perry, and Beyonce are among the musical artists most represented by the Grammys as of late. This is one of the reasons I can't watch the Grammys, I don't listen to any of the new artists. Spinal Tap sounds better than a lot of what reaches Billboard these days. Now I'm not one of those retrophiles who hates new music simply because it's new. I love Nujabes, Airborne Toxic Event, DJ Okawari, and I even think that Kyary Pamyu Pamyu is a little catchy. The singles "Rolling In The Deep" by Adele and "One Engine" by The Decemberists are excellent, while The Foo Fighters' Wasting Light was a great rock album. Simply because I don't particularly like any of the artists who often win Grammys doesn't make me better than those who do, it simply means that the Grammys aren't for me.

I referenced part of this problem earlier. Even if an artist I enjoyed was getting recognition, I'd have to wade through a bunch of other artists who I don't care for just to get there. The Video Music Awards may supposedly represent my generation, but they don't represent all of us.

This even happens with the high-brow Academy. After all, how many of you actually saw Slumdog Millionaire, Nebraska, An Education, The Reader, or Michael Clayton before they were nominated for Oscars? Though, yes, these awards can help bring public attention to those lesser known films (which is a good thing), but again, is it necessary to watch the show just to get that? I think the press releases, critical reviews, and film festivals can get that much accomplished.

Don't even get me started on the Tony's. I know that I couldn't afford to see all of those shows on Broadway. Could you?

All of the results will be available online after the show.

I've hinted at this point before, but it needs repeating.

It's not as if, if you miss the show or forget to record it that you'll never get the results. You could easily save hours out of your evening and just get the results from Google. That's what it's all about, isn't it? The results: who won and who lost. Still want to see acceptance speeches or performances? Fine, look them up on YouTube. See how much time you've saved.

Again, I don't see the larger point in watching these award shows, you really get nothing out of it, aside from a chance to drool over your favorite celebrities. There's a channel for that, it's called TMZ, but I wouldn't recommend that you watch it.

I recognize that this essay has been rather, well, short, compared to my others, and I suppose it's because I don't feel the need to waste too much ink on convincing the Average Joe that watching celebrities congratulate each other is something that we see every day. No need to turn it into a televised event. That's just masturbation.

Bibliography:

"One Direction Wins Favorite Band At People's Choice Awards 2014" Perez Hilton. January 9, 2014. www.perezhilton.com

Pastorek, Whitney. "The People's Choice Awards: You showed up? Here's a trophy!" Entertainment Weekly. January 8, 2009. popwatch.ew.com

Sunday, February 23, 2020

Eye On Kickstarter #80

Welcome to my Eye on Kickstarter series! This series will highlight Kickstarter campaigns I am following that have recently launched (or I've recently discovered) because they have caught my interest. Usually they'll catch my interest because they look like great games that I have either backed or would like to back (unfortunately budget doesn't allow me to back everything I'd like to). But occasionally the campaigns caught my attention for other reasons. Twice a month, on the 2nd and 4th Fridays, I'll make a new post in this series, highlighting the campaigns that have caught my attention since the last post. In each post I'll highlight one campaign that has really grabbed my attention, followed by other campaigns I've backed or am interested in. I'll also include links to any related reviews or interviews I've done. Comments are welcome, as are suggestions for new campaigns to check out!

You can also see my full Kickstarter Profile to see what I've backed or my old Eye on Kickstarter page that was too unwieldy to maintain. Also, check out the 2019 Kickstarter Boardgame Projects geeklist over on Board Game Geek for a list of all the tabletop games of the year.

Support me on Patreon!

So, without further ado, here are the projects I'm currently watching as of the fourth Friday of January, 2020:

Live Campaigns from Past Eyes:
APEX: Theropod Deck-Building Game by Outland Entertainment

HIGHLIGHTED CAMPAIGN
School of Sorcery
by Dr. Finn's Games - 2 DAYS LEFT!

Steve Finn has a reputation for designing amazing filler games, and, having played a number of them, I have to say the reputation is well deserved. His games are always fast, light, engaging, and interesting. School of Sorcery is his latest Kickstarter for an update to an older game of his (2015's Institute for Magical Arts) with new artwork and refined mechanics. Go ahead and grab this, I'm sure you won't be disappointed! And hurry, this was a 2 week campaign, so there's only a few hours left!

Collect magical items and build friendships to increase your influence at the School of Sorcery. This quick-paced strategic game for 2 players is a newly revised and updated version of Dr Finn's popular Institute for Magical Arts, successfully Kickstarted in 2015.

During the game, players compete to win magical items and characters at different locations. Each round has 5 Phases:
PHASE I: Collect Crystals - Collect 5 crystals
PHASE II: Cast Crystals - Using your dice roll, send crystals to desired locations
PHASE III: Use Portal - Using the portal, secretly send crystals to a new location
PHASE IV: Activate Powers - Activate the powers of your permanent cards
PHASE V: Evaluate Locations - Award sorcery cards at the locations

Features:
Simultaneous Decision-Making: Little downtime
Dice Mitigation: Multiple options for every dice roll
Unique Card Powers: Increases replayability
Fantastic Art and Colorful Graphics: nice to look at
Quality Components: shiny gems, punchboard player boards and locations, wooden tokens, high quality cardstock

Pacific Rails
by Vesuvius Media

Pacific Rails is a train game with a theme that I really find interesting. It's about the construction of the Transcontinental Railroad, which I also coincidentally made a game about (see The Overland Route), though it's just a small 2-player, 18-card game. Pacific Rails looks like a fun, historical tile game and I'd love to give it a try someday.

Return to Dark Tower
by Restoration Games

Restoration Games has been breathing new life into classic games from decades ago. Last year's hit for them was a reboot of Fireball Island and now they're back with a completely reworked and modernized version of the classic Dark Tower. In this new iteration they've improved the gameplay, updated the art and graphics, blended the physical gameplay with an innovative app, and engineered an amazing tower that actually works in conjunction with the app via bluetooth. I just wish it wasn't so darn expensive! Still probably cheaper than getting your hands on a good copy of the original though...

Jurassic Parts
by 25th Century Games

I love puzzle and area control games and Jurassic Parts looks like a great one. Not because of the dinosaur theme, which is great, but because of the interesting area control puzzle aspect of it. As you use chisels to section off pieces of map you'll get rewarded based on how much you helped with sectioning off the map. The more you help the more fossils you'll get, so there's a really interesting area control battle along with a puzzle as you try to figure out how to best section out the play area so you can build the most complete skeletons.

Migration Mars
by Enhance Games

I've mentioned before that I'm a sucker for a great space themed game, especially with roots in actual science. Migration Mars is about the race to build a sustainable human colony on Mars and it looks incredible. From the amazing buildings to the clean graphics, this is a game I'd love to get to the table.

Citadel Deck Block
by Quiver Time

Back in 2016 I reviewed the Quiver Gaming Case and thought it was outstanding. I still use it to this day to store and carry my Star Realms, Epic, and some other card games. Now the team is back with a deck box that is just as, or maybe even more incredible. The quality on Quiver Time's products is amazing and the engineering that has gone into the Citadel has resulted in an ingenious product. If you're looking for a high quality deck box, here's your answer!

Thursday, February 20, 2020

(270 MB) Download Hitman 4 Blood Money Highly Compressed For Pc

Screenshot

Hitman 4 Blood Money System Requirements

Following are the minimum system requirements of Hitman 4 Blood Money.

Operating system: Windows XP, Vista, Windows 7, Windows 8 and 8.1
Processor: Core 2 Duo
Setup Size: 270 MB
Ram: 1GB
Hard disk space: 5 GB

Small Modern House + DOWNLOAD + TOUR + CC CREATORS | The Sims 4 |

Tech Book Face Off: Programming Massively Parallel Processors Vs. Professional CUDA C Programming

After getting an introduction to GPU programming with CUDA by Example, I wanted to dig in deeper and get to know the real ins and outs of CUDA programming. That desire quickly lead to the selection of books for this Tech Book Face Off. The first book is definitely geared to be a college textbook, and as I spent years learning from books like this, I felt comfortable taking a look at Programming Massively Parallel Processors: A Hands-on Approach by David B. Kirk and Wen-mei W. Hwu. The second book is targeted more at the working professional, as the title suggests: Professional CUDA C Programming by John Cheng, Max Grossman, and Ty McKercher. I was surprised by both books, and not in the same way. Let's see how they do at teaching CUDA programming.

Programming Massively Parallel Multiprocessors front cover

VS.

Professional CUDA C Programming front cover

Programming Massively Parallel Processors

The polite way to critique this book is to say, it's somewhat verbose and repetitive, but if you can get past that, it has a lot to offer in the way of example CUDA programs that show how to optimize code for the GPU architecture. A slightly less polite way to say that would be that while this book does offer some good code examples, the writing leaves much to be desired, and much better books are out there that cover the same material. The honest assessment is that this book is just a mess. Half the book could be cut and the other half rewritten to better explain things with clearer, non-circular definitions. The only good thing about the book is the code examples, and many of those examples are also redundant, filling the pages of the book with lines of code that the reader has seen multiple times before. This book could have been a third the length and covered the same amount of material.

Even though that last bit was a pretty harsh review, we should still explore what's in the book, if only to see how the breadth of material compares to Professional CUDA C Programming. The first chapter is the normal introduction to the book's material, describing the architecture of a GPU and discussing how parallel programming with this architecture is so different than programming on a CPU. The verbosity of this chapter alone should have been a clue that this book would drag on and on, but I was willing to give it a chance. The next chapter introduces our first real CUDA program with a vector addition kernel. We're still getting started with CUDA C at this point, so I chalk up the authors' overly detailed explanations to taking extra care with novice readers. We end up walking through all of the parts of a working CUDA program, explaining everything in excruciating detail.

The third chapter covers how to work more efficiently with threads and loading data into GPU memory from the CPU with a more complex example of calculating image blur. We also get our first exposure to thread synchronization, something that must be thoroughly understood to program GPUs effectively. This chapter is also where I start to realize how nutty some of the explanations are getting. Here's just one example of them describing how arrays are laid out in memory:

A two-dimensional array can be linearized in at least two ways. One way is to place all elements of the same row into consecutive locations. The rows are then placed one after another into the memory space. This arrangement, called row-major layout, is depicted in Fig. 3.3. To improve readability, we will use M_j,i to denote the M element at the jth row and the ith column. P_j,i is equivalent to the C expression M[j][i] but is slightly more readable. Fig. 3.3 illustrates how a 4×4 matrix M is linearized into a 16-element one-dimensional array, with all elements of row 0 first, followed by the four elements of row 1, and so on. Therefore, the one-dimensional equivalent index for M in row j and column i is j*4 +i. The j*4 term skips all elements of the rows before row j. The i term then selects the right element within the section for row j. The one-dimensional index for M_2,1 is 2*4 +1 =9, as shown in Fig. 3.3, where M₉ is the one-dimensional equivalent to M_2,1. This process shows the way C compilers linearize two-dimensional arrays.

Wow. I'm not sure how a reader that needs this level of detail for understanding how a matrix is arranged in memory is going to understand the memory hierarchy and synchronization issues of GPU programming. This explanation is just too much for a book like this. Readers should already have some knowledge of standard C programming, including multi-dimensional array memory layout, before attempting CUDA programming. I can't imagine learning both at the same time going very well. As for readers who already know how all of this stuff works, they could easily skip every other paragraph and skim the rest to make trudging through these explanations more tolerable.

The next chapter is on how to manage memory and arrange data access to optimize memory usage and bandwidth. We find that memory management is just as, if not more important than thread management for making optimal use of the GPU computing resources, and the book solidifies this understanding through an extended optimization example of a matrix multiplication kernel.

At this point we've learned the fundamentals of GPU programming, so the next chapter moves into more advanced topics in performance optimization with the memory hierarchy and the compute core architecture. Then, chapter six covers number format considerations between integers and single- and double-precision floating point representations. The authors' definition of representable numbers struck me as exceptionally cringe-worthy here:

The representable numbers of a representation format are the numbers that can be exactly represented in the format.

This is but one example of their impenetrable and useless definitions. More often than not, I found that if I hadn't already known what they were talking about, their discussions would provide no further illumination.

Now we get into the halfway decent part of the book, the extended example chapters on parallel patterns. Each of these chapters works through a different example kernel of a particular problem that comes up often in parallel programming, and they introduce additional features of GPU programming that can assist in solving these problems in a more optimal way. The contents of these chapters are as follows:

Chapter 7: Convolution
Chapter 8: Prefix Sum (Accumulator)
Chapter 9: Parallel Histogram Calculation
Chapter 10: Sparse Matrix Computation
Chapter 11: Merge Sort
Chapter 12: Graph Search

As long as you skim the descriptions of the problems and solutions, and focus on understanding the code yourself, these chapters are quite useful examples of how to write performant parallel programs with CUDA. However, the authors continue to suffer from what seems to be a mis-interpretation of the phrase, "a picture is worth a thousand words." For every diagram they use, they also include a thousand words or more of explanation, describing the diagrams ad nauseam.

The next chapter covers how to kick off kernels from within other kernels in order to enable dynamic parallelism. Up until this point, all kernels have been launched from the host (CPU) code, but it is possible to have kernels launch other kernels to take advantage of additional parallelism while the code is executing on the GPU, an effective feature for some algorithms. Then, the next three chapters are fairly useful application case studies. Like the parallel pattern example chapters, these chapters use CUDA code to show how to take advantage of more advanced features of the GPU, and how to put together everything we've learned so far to optimize some example parallel programs. The applications described are for non-Cartesian MRI, molecular visualization and analysis, and machine learning neural networks, so nice, interesting topics for GPU programming.

The last five chapters were either more drudgery or topics I wasn't interested in, so I skipped them and called it quits for this long and tedious book. For completeness, those chapters are on how to think when parallel programming (so a pep talk on what to think about from authors that couldn't clearly describe much else in the book), multi-GPU programming, OpenACC (another GPU programming framework, like CUDA), still more performance considerations, and a summary chapter.

I couldn't bring myself to keep reading chapters that wouldn't amount to anything, so I put down the book after finishing the last chapter on application case studies. I found that chapters seven through sixteen contained most of the useful information in the book, but the introduction to CUDA programming was too verbose and confusing. There are much better books out there for learning that part of CUDA programming. Case in point: CUDA by Example or the next book in this review.

Professional CUDA C Programming

Unlike the last book, I was surprised by how readable this book was. The authors did an excellent job of presenting concepts in CUDA programming in a clear, direct, and succinct manner. They also did this without resorting to humor, which can sometimes work if the author is an excellent writer, but it often feels forced and lame when done poorly. It's better to stick to clear descriptions and tight writing, as these authors did quite well. I was actually disappointed that I didn't read this book first, instead saving it until last, because it did the best job of explaining all of the CUDA programming concepts while covering essentially the same material as Programming Massively Parallel Processors and certainly more than CUDA by Example.

The first chapter is the obligatory introduction to CUDA with the requisite Hello, World program showing how to run code on the GPU. Right away, we can see how well-written the descriptions are with this discussion of how parallel programming is different than sequential programming:

When implementing a sequential algorithm, you may not need to understand the details of the computer architecture to write a correct program. However, when implementing algorithms for multicore machines, it is much more important for programmers to be aware of the characteristics of the underlying computer architecture. Writing both correct and efficient parallel programs requires a fundamental knowledge of multicore architectures.

We need to be prepared to think differently about problems when parallel programming, and we're going to have to learn the architecture of the underlying hardware to make full use of it. That leads us right into chapter 2, where we learn about the CUDA programming model and how to organize threads on the device, but it doesn't end there. Throughout the book we're learning more and more about the nVidia GPU architecture (specifically the older Fermi and Kepler architectures, since those were available at the time of the book's writing) in order to take full advantage of its compute power. I like how the authors grounded their discussions in specific GPU architectures and showed how the architecture was evolving from one generation to the next. I'm sure the newer Pascal, Volta, and Turing architectures provide more advanced and flexible features, but the book builds a great foundation. Chapter 2 also contains the clearest definition of a kernel that I've seen, yet:

A kernel function is the code to be executed on the device side. In a kernel function, you define the computation for a single thread, and the data access for that thread. When the kernel is called, many different CUDA threads perform the same computation in parallel.

This explanation is the essence of the paradigm shift from sequential to parallel programming, and it's important to understand the effect it has on the code that you write and how it runs on the hardware. In addition to the excellent writing, each chapter has some nice exercises at the end. That's not normally something you find in programming books like this. Exercises seem to be left to textbooks, like Programming Massively Parallel Processors, which had them as well, but in Professional CUDA C Programming they're more well-conceived and more relevant.

The next chapter covers the CUDA execution model, or how the code runs on the real hardware. Here is where we learn how to optimize CUDA programs to take advantage of all of those independent compute cores on the GPU, and this chapter even gets into dynamic parallelism earlier in the book rather than waiting and treating it as a special topic like the last book did.

Chapter 4 covers global memory and chapter 5 looks at shared and constant memory. Understanding the trade-offs of each of these memories is important to getting the maximum performance out of the GPU because most often these programs are memory-bound, not compute-bound. Like everything else, the authors do an excellent job explaining the memory hierarchy and how those trade-offs affect CUDA programs. The examples used throughout the book are simple so that the reader doesn't get bogged down trying to understand unrelated algorithm details. The more complex examples may be thought-provoking, but simple examples do a good job of showcasing the specifics of the topic at hand.

Chapter 6 addresses streams and events, which are used to overlap computation with data transfer. Using streams can partially, or in some cases completely hide the time it takes to get the data into the GPU memory. Chapter 7 explains more optimization techniques by using CUDA instruction-level primitives to directly control how computations are performed on the GPU. These instructions trade some accuracy for speed, and they should be used only if the accuracy is not critical to the application. The authors do a good job of explaining all of the implications here.

The last three chapters weren't as interesting to me, not because I was tired of the book this time, but because they were about the same topics that I skipped in the other CUDA books: OpenACC, multi-GPU programming, and the CUDA development process. The rest of the book was excellent, and far better than the other two CUDA books I read. The writing is clear with plenty of diagrams for better understanding of each topic, and the book organization is done well. If you're interested in GPU programming and want to read one book, this one is it.

Between these two CUDA books, the choice is obvious. Programming Massively Parallel Processors was a bloated dud. It may be worth it just for the large set of example programs it contains, but there are other options coming down the pipeline for that kind of cookbook that may be better. Professional CUDA C Programming was better in every way, and really the book to get for learning CUDA programming. The authors did a great job of explaining complex topics in GPU architecture with concise, understandable writing, relevant diagrams, and appropriate exercises for practice. It's exactly the kind of book I want for learning a new programming language, or in this case, programming paradigm. If you're at all interested in CUDA programming, it's worth checking out.