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Textricator：让数据提取变得简单
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======
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> 这个新的开源工具可以从 PDF 文档中提取复杂的数据，而无需编程技能。
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你可能知道这种感觉：你请求数据并得到积极的响应，只打开电子邮件并发现一大堆附加的 PDF。数据中断。
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你可能知道这种感觉：你请求得到数据并得到积极的响应，只打开电子邮件并发现一大堆附加的 PDF。数据——中断。
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我们理解你的挫败感，我们已经做了一些事情：介绍下 [Textricator][1]，我们的第一个开源产品。
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我们理解你的挫败感，并为此做了一些事情：让我们介绍下 [Textricator][1]，这是我们的第一个开源产品。
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我们是 “Measures for Justice”，一个刑事司法研究和透明度组织。我们的使命是为整个司法系统从逮捕到定罪后提供数据透明度。我们通过制定一系列多达 32 项绩效指标来实现这一目标，涵盖整个县的刑事司法系统。我们以多种方式获取数据 - 当然，所有这些都是合法的 - 虽然许多州和县机构都掌握数据，为我们提供 CSV 格式的高质量格式化数据，但这些数据通常捆绑在软件中，没有简单的方法可以提取。PDF 报告是他们能提供的最佳报告。
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我们是 “Measures for Justice”（MFJ），一个刑事司法研究和透明度组织。我们的使命是为整个司法系统从逮捕到定罪后提供数据透明度。我们通过制定一系列多达 32 项指标来实现这一目标，涵盖每个县的整个刑事司法系统。我们以多种方式获取数据 —— 当然，所有这些都是合法的 —— 虽然许多州和县机构都掌握数据，可以为我们提供 CSV 格式的高质量格式化数据，但这些数据通常捆绑在软件中，没有简单的方法可以提取。PDF 报告是他们能提供的最佳报告。
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开发者 Joe Hale 和 Stephen Byrne 在过去两年中一直在开发 Textricator，它用来提取内部使用的数万页数据。Textricator 可以处理几乎任何基于文本的 PDF 格式 - 不仅仅是表格，还包括复杂的报表，其中包含从 Crystal Reports 等工具生成的文本和细节部分。只需告诉 Textricator 你要收集的字段的属性，它就会整理文档，收集并写出你的记录。
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开发者 Joe Hale 和 Stephen Byrne 在过去两年中一直在开发 Textricator，它用来提取数万页数据供我们内部使用。Textricator 可以处理几乎任何基于文本的 PDF 格式 —— 不仅仅是表格，还包括复杂的报表，其中包含从 Crystal Reports 等工具生成的文本和细节部分。只需告诉 Textricator 你要收集的字段的属性，它就会整理文档，收集并写出你的记录。
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不是软件工程师？Textricator 不需要编程技巧。相反，用户描述 PDF 的结构，Textricator 处理其余部分。大多数用户通过命令行运行它。但是，你可以使用基于浏览器的 GUI。
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我们评估了其他很好的开源解决方案，如 [Tabula][2]，但他们无法处理我们需要抓取的一些 PDF 的结构。技术总监 Andrew Branch 说：“Textricator 既灵活又强大，缩短了我们花费大量时间处理大型数据集的时间。”
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我们评估了其他很好的开源解决方案，如 [Tabula][2]，但它们无法处理我们需要抓取的一些 PDF 的结构。技术总监 Andrew Branch 说：“Textricator 既灵活又强大，缩短了我们花费大量时间处理大型数据集的时间。”
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在 MFJ，我们致力于透明度和知识共享，其中包括向任何人提供我们的软件，特别是那些试图公开免费共享数据的人。Textricator 可以在 [GitHub][3] 上找到，并在 [GNU Affero 通用公共许可证第 3 版][4]下发布。
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在 MFJ，我们致力于透明度和知识共享，其中包括向任何人提供我们的软件，特别是那些试图公开自由共享数据的人。Textricator 可以在 [GitHub][3] 上找到，并在 [GNU Affero 通用公共许可证第 3 版][4]下发布。
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你可以在我们的免费[在线数据门户][5]上查看我们的工作成果，包括通过 Textricator 处理的数据。Textricator 是我们流程的重要组成部分，我们希望民间技术机构和政府组织都可以使用这个新工具解锁更多数据。
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如果你使用 Textricator，请告诉我们它如何帮助你解决数据问题。想要改进吗？提交一个 pull request。
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如果你使用 Textricator，请告诉我们它如何帮助你解决数据问题。想要改进吗？提交一个拉取请求。
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--------------------------------------------------------------------------------
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@ -28,11 +30,11 @@ via: https://opensource.com/article/18/7/textricator
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作者：[Stephen Byrne][a]
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选题：[lujun9972](https://github.com/lujun9972)
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译者：[geekpi](https://github.com/geekpi)
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校对：[校对者ID](https://github.com/校对者ID)
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校对：[wxy](https://github.com/wxy)
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本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]:
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[a]:https://opensource.com/users/stephenbyrne-mfj
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[1]:https://textricator.mfj.io/
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[2]:https://tabula.technology/
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[3]:https://github.com/measuresforjustice/textricator
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pinewall translating
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How Graphics Cards Work
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======
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![AMD-Polaris][1]
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Ever since 3dfx debuted the original Voodoo accelerator, no single piece of equipment in a PC has had as much of an impact on whether your machine could game as the humble graphics card. While other components absolutely matter, a top-end PC with 32GB of RAM, a $500 CPU, and PCIe-based storage will choke and die if asked to run modern AAA titles on a ten year-old card at modern resolutions and detail levels. Graphics cards (also commonly referred to as GPUs, or graphics processing units) are critical to game performance and we cover them extensively. But we don’t often dive into what makes a GPU tick and how the cards function.
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By necessity, this will be a high-level overview of GPU functionality and cover information common to AMD, Nvidia, and Intel’s integrated GPUs, as well as any discrete cards Intel might build in the future. It should also be common to the mobile GPUs built by Apple, Imagination Technologies, Qualcomm, ARM, and other vendors.
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### Why Don’t We Run Rendering With CPUs?
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The first point I want to address is why we don’t use CPUs for rendering workloads in gaming in the first place. The honest answer to this question is that you can run rendering workloads directly on a CPU, at least in theory. Early 3D games that predate the widespread availability of graphics cards, like Ultima Underworld, ran entirely on the CPU. UU is a useful reference case for multiple reasons — it had a more advanced rendering engine than games like Doom, with full support for looking up and down, as well as then-advanced features like texture mapping. But this kind of support came at a heavy price — many people lacked a PC that could actually run the game.
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In the early days of 3D gaming, many titles like Half Life and Quake II featured a software renderer to allow players without 3D accelerators to play the title. But the reason we dropped this option from modern titles is simple: CPUs are designed to be general-purpose microprocessors, which is another way of saying they lack the specialized hardware and capabilities that GPUs offer. A modern CPU could easily handle titles that tended to stutter when run in software 18 years ago, but no CPU on Earth could easily handle a modern AAA game from today if run in that mode. Not, at least, without some drastic changes to the scene, resolution, and various visual effects.
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### What’s a GPU?
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A GPU is a device with a set of specific hardware capabilities that are intended to map well to the way that various 3D engines execute their code, including geometry setup and execution, texture mapping, memory access, and shaders. There’s a relationship between the way 3D engines function and the way GPU designers build hardware. Some of you may remember that AMD’s HD 5000 family used a VLIW5 architecture, while certain high-end GPUs in the HD 6000 family used a VLIW4 architecture. With GCN, AMD changed its approach to parallelism, in the name of extracting more useful performance per clock cycle.
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Nvidia first coined the term “GPU” with the launch of the original GeForce 256 and its support for performing hardware transform and lighting calculations on the GPU (this corresponded, roughly to the launch of Microsoft’s DirectX 7). Integrating specialized capabilities directly into hardware was a hallmark of early GPU technology. Many of those specialized technologies are still employed (in very different forms), because it’s more power efficient and faster to have dedicated resources on-chip for handling specific types of workloads than it is to attempt to handle all of the work in a single array of programmable cores.
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There are a number of differences between GPU and CPU cores, but at a high level, you can think about them like this. CPUs are typically designed to execute single-threaded code as quickly and efficiently as possible. Features like SMT / Hyper-Threading improve on this, but we scale multi-threaded performance by stacking more high-efficiency single-threaded cores side-by-side. AMD’s 32-core / 64-thread Epyc CPUs are the largest you can buy today. To put that in perspective, the lowest-end Pascal GPU from Nvidia has 384 cores. A “core” in GPU parlance refers to a much smaller unit of processing capability than in a typical CPU.
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**Note:** You cannot compare or estimate relative gaming performance between AMD and Nvidia simply by comparing the number of GPU cores. Within the same GPU family (for example, Nvidia’s GeForce GTX 10 series, or AMD’s RX 4xx or 5xx family), a higher GPU core count means that GPU is more powerful than a lower-end card.
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The reason you can’t draw immediate conclusions on GPU performance between manufacturers or core families based solely on core counts is because different architectures are more and less efficient. Unlike CPUs, GPUs are designed to work in parallel. Both AMD and Nvidia structure their cards into blocks of computing resources. Nvidia calls these blocks an SM (Streaming Multiprocessor), while AMD refers to them as a Compute Unit.
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Each block contains a group of cores, a scheduler, a register file, instruction cache, texture and L1 cache, and texture mapping units. The SM / CU can be thought of as the smallest functional block of the GPU. It doesn’t contain literally everything — video decode engines, render outputs required for actually drawing an image on-screen, and the memory interfaces used to communicate with onboard VRAM are all outside its purview — but when AMD refers to an APU as having 8 or 11 Vega Compute Units, this is the (equivalent) block of silicon they’re talking about. And if you look at a block diagram of a GPU, any GPU, you’ll notice that it’s the SM/CU that’s duplicated a dozen or more times in the image.
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The higher the number of SM/CU units in a GPU, the more work it can perform in parallel per clock cycle. Rendering is a type of problem that’s sometimes referred to as “embarrassingly parallel,” meaning it has the potential to scale upwards extremely well as core counts increase.
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When we discuss GPU designs, we often use a format that looks something like this: 4096:160:64. The GPU core count is the first number. The larger it is, the faster the GPU, provided we’re comparing within the same family (GTX 970 versus GTX 980 versus GTX 980 Ti, RX 560 versus RX 580, and so on).
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### Texture Mapping and Render Outputs
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There are two other major components of a GPU: texture mapping units and render outputs. The number of texture mapping units in a design dictates its maximum texel output and how quickly it can address and map textures on to objects. Early 3D games used very little texturing, because the job of drawing 3D polygonal shapes was difficult enough. Textures aren’t actually required for 3D gaming, though the list of games that don’t use them in the modern age is extremely small.
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The number of texture mapping units in a GPU is signified by the second figure in the 4096:160:64 metric. AMD, Nvidia, and Intel typically shift these numbers equivalently as they scale a GPU family up and down. In other words, you won’t really find a scenario where one GPU has a 4096:160:64 configuration while a GPU above or below it in the stack is a 4096:320:64 configuration. Texture mapping can absolutely be a bottleneck in games, but the next-highest GPU in the product stack will typically offer at least more GPU cores and texture mapping units (whether higher-end cards have more ROPs depends on the GPU family and the card configuration).
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Render outputs (also sometimes called raster operations pipelines) are where the GPU’s output is assembled into an image for display on a monitor or television. The number of render outputs multiplied by the clock speed of the GPU controls the pixel fill rate. A higher number of ROPs means that more pixels can be output simultaneously. ROPs also handle antialiasing, and enabling AA — especially supersampled AA — can result in a game that’s fill-rate limited.
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### Memory Bandwidth, Memory Capacity
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The last components we’ll discuss are memory bandwidth and memory capacity. Memory bandwidth refers to how much data can be copied to and from the GPU’s dedicated VRAM buffer per second. Many advanced visual effects (and higher resolutions more generally) require more memory bandwidth to run at reasonable frame rates because they increase the total amount of data being copied into and out of the GPU core.
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In some cases, a lack of memory bandwidth can be a substantial bottleneck for a GPU. AMD’s APUs like the Ryzen 5 2400G are heavily bandwidth-limited, which means increasing your DDR4 clock rate can have a substantial impact on overall performance. The choice of game engine can also have a substantial impact on how much memory bandwidth a GPU needs to avoid this problem, as can a game’s target resolution.
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The total amount of on-board memory is another critical factor in GPUs. If the amount of VRAM needed to run at a given detail level or resolution exceeds available resources, the game will often still run, but it’ll have to use the CPU’s main memory for storing additional texture data — and it takes the GPU vastly longer to pull data out of DRAM as opposed to its onboard pool of dedicated VRAM. This leads to massive stuttering as the game staggers between pulling data from a quick pool of local memory and general system RAM.
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One thing to be aware of is that GPU manufacturers will sometimes equip a low-end or midrange card with more VRAM than is otherwise standard as a way to charge a bit more for the product. We can’t make an absolute prediction as to whether this makes the GPU more attractive because honestly, the results vary depending on the GPU in question. What we can tell you is that in many cases, it isn’t worth paying more for a card if the only difference is a larger RAM buffer. As a rule of thumb, lower-end GPUs tend to run into other bottlenecks before they’re choked by limited available memory. When in doubt, check reviews of the card and look for comparisons of whether a 2GB version is outperformed by the 4GB flavor or whatever the relevant amount of RAM would be. More often than not, assuming all else is equal between the two solutions, you’ll find the higher RAM loadout not worth paying for.
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Check out our [ExtremeTech Explains][2] series for more in-depth coverage of today’s hottest tech topics.
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--------------------------------------------------------------------------------
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via: https://www.extremetech.com/gaming/269335-how-graphics-cards-work
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作者：[Joel Hruska][a]
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选题：[lujun9972](https://github.com/lujun9972)
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译者：[译者ID](https://github.com/译者ID)
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校对：[校对者ID](https://github.com/校对者ID)
|
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|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
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[a]:https://www.extremetech.com/author/jhruska
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[1]:https://www.extremetech.com/wp-content/uploads/2016/07/AMD-Polaris-640x353.jpg
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[2]:http://www.extremetech.com/tag/extremetech-explains
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@ -1,103 +0,0 @@
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Translating by qhwdw
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Bitcoin is a Cult — Adam Caudill
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======
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The Bitcoin community has changed greatly over the years; from technophiles that could explain a [Merkle tree][1] in their sleep, to speculators driven by the desire for a quick profit & blockchain startups seeking billion dollar valuations led by people who don’t even know what a Merkle tree is. As the years have gone on, a zealotry has been building around Bitcoin and other cryptocurrencies driven by people who see them as something far grander than they actually are; people who believe that normal (or fiat) currencies are becoming a thing of the past, and the cryptocurrencies will fundamentally change the world’s economy.
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Every year, their ranks grow, and their perception of cryptocurrencies becomes more grandiose, even as [novel uses][2] of the technology brings it to its knees. While I’m a firm believer that a well designed cryptocurrency could ease the flow of money across borders, and provide a stable option in areas of mass inflation, the reality is that we aren’t there yet. In fact, it’s the substantial instability in value that allows speculators to make money. Those that preach that the US Dollar and Euro are on their deathbed have utterly abandoned an objective view of reality.
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### A little background…
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I read the Bitcoin white-paper the day it was released – an interesting use of [Merkle trees][1] to create a public ledger and a fairly reasonable consensus protocol – it got the attention of many in the cryptography sphere for its novel properties. In the years since that paper was released, Bitcoin has become rather valuable, attracted many that see it as an investment, and a loyal (and vocal) following of people who think it’ll change everything. This discussion is about the latter.
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Yesterday, someone on Twitter posted the hash of a recent Bitcoin block, the thousands of Tweets and other conversations that followed have convinced me that Bitcoin has crossed the line into true cult territory.
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It all started with this Tweet by Mark Wilcox:
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> #00000000000000000021e800c1e8df51b22c1588e5a624bea17e9faa34b2dc4a
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> — Mark Wilcox (@mwilcox) June 19, 2018
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The value posted is the hash of [Bitcoin block #528249][3]. The leading zeros are a result of the mining process; to mine a block you combine the contents of the block with a nonce (and other data), hash it, and it has to have at least a certain number of leading zeros to be considered valid. If it doesn’t have the correct number, you change the nonce and try again. Repeat this until the number of leading zeros is the right number, and you now have a valid block. The part that people got excited about is what follows, 21e800.
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Some are claiming this is an intentional reference, that whoever mined this block actually went well beyond the current difficulty to not just bruteforce the leading zeros, but also the next 24 bits – which would require some serious computing power. If someone had the ability to bruteforce this, it could indicate something rather serious, such as a substantial breakthrough in computing or cryptography.
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You must be asking yourself, what’s so important about 21e800 – a question you would surely regret. Some are claiming it’s a reference to [E8 Theory][4] (a widely criticized paper that presents a standard field theory), or to the 21,000,000 total Bitcoins that will eventually exist (despite the fact that `21 x 10^8` would be 2,100,000,000). There are others, they are just too crazy to write about. Another important fact is that a block is mined on average on once a year that has 21e8 following the leading zeros – those were never seen as anything important.
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This leads to where things get fun: the [theories][5] that are circulating about how this happened.
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* A quantum computer, that is somehow able to hash at unbelievable speed. This is despite the fact that there’s no indication in theories around quantum computers that they’ll be able to do this; hashing is one thing that’s considered safe from quantum computers.
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* Time travel. Yes, people are actually saying that someone came back from the future to mine this block. I think this is crazy enough that I don’t need to get into why this is wrong.
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* Satoshi Nakamoto is back. Despite the fact that there has been no activity with his private keys, some theorize that he has returned, and is somehow able to do things that nobody can. These theories don’t explain how he could do it.
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> So basically (as i understand) Satoshi, in order to have known and computed the things that he did, according to modern science he was either:
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>
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> A) Using a quantum computer
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> B) Fom the future
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> C) Both
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>
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> — Crypto Randy Marsh [REKT] (@nondualrandy) [June 21, 2018][6]
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If all this sounds like [numerology][7] to you, you aren’t alone.
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All this discussion around special meaning in block hashes also reignited the discussion around something that is, at least somewhat, interesting. The Bitcoin genesis block, the first bitcoin block, does have an unusual property: the early Bitcoin blocks required that the first 32 bits of the hash be zero; however the genesis block had 43 leading zero bits. As the code that produced the genesis block was never released, it’s not known how it was produced, nor is it known what type of hardware was used to produce it. Satoshi had an academic background, so may have had access to more substantial computing power than was common at the time via a university. At this point, the oddities of the genesis block are a historical curiosity, nothing more.
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### A brief digression on hashing
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This hullabaloo started with the hash of a Bitcoin block; so it’s important to understand just what a hash is, and understand one very important property they have. A hash is a one-way cryptographic function that creates a pseudo-random output based on the data that it’s given.
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What this means, for the purposes of this discussion, is that for each input you get a random output. Random numbers have a way of sometimes looking interesting, simply as a result of being random and the human brain’s affinity to find order in everything. When you start looking for order in random data, you find interesting things – that are yet meaningless, as it’s simply random. When people ascribe significant meaning to random data, it tells you far more about the mindset of those involved rather than the data itself.
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### Cult of the Coin
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First, let us define a couple of terms:
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* Cult: a system of religious veneration and devotion directed toward a particular figure or object.
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* Religion: a pursuit or interest to which someone ascribes supreme importance.
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The Cult of the Coin has many saints, perhaps none greater than Satoshi Nakamoto, the pseudonym used by the person(s) that created Bitcoin. Vigorously defended, ascribed with ability and understanding far above that of a normal researcher, seen as a visionary beyond compare that is leading the world to a new economic order. When combined with Satoshi’s secretive nature and unknown true identify, adherents to the Cult view Satoshi as a truly venerated figure.
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That is, of course, with the exception of adherents that follow a different saint, who is unquestionably correct, and any criticism is seen as not only an attack on their saint, but on themselves as well. Those that follow EOS for example, may see Satoshi has a hack that developed a failed project, yet will react fiercely to the slightest criticism of EOS, a reaction so strong that it’s reserved only for an attack on one’s deity. Those that follow IOTA react with equal fierceness; and there are many others.
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These adherents have abandoned objectivity and reasonable discourse, and allowed their zealotry to cloud their vision. Any discussion of these projects and the people behind them that doesn’t include glowing praise inevitably ends with a level of vitriolic speech that is beyond reason for a discussion of technology.
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This is dangerous, for many reasons:
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* Developers & researchers are blinded to flaws. Due to the vast quantities of praise by adherents, those involved develop a grandiose view of their own abilities, and begin to view criticism as unjustified attacks – as they couldn’t possibly have been wrong.
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* Real problems are attacked. Instead of technical issues being seen as problems to be solved and opportunities to improve, they are seen as attacks from people who must be motivated to destroy the project.
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* One coin to rule them all. Adherents are often aligned to one, and only one, saint. Acknowledging the qualities of another project means acceptance of flaws or deficiencies in their own, which they will not do.
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* Preventing real progress. Evolution is brutal, it requires death, it requires projects to fail and that the reasons for those failures to be acknowledged. If lessons from failure are ignored, if things that should die aren’t allowed to, progress stalls.
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|
||||
Discussions around many of the cryptocurrencies and related blockchain projects are becoming more and more toxic, becoming impossible for well-intentioned people to have real technical discussions without being attacked. With discussions of real flaws, flaws that would doom a design in any other environment, being instantly treated as heretical without any analysis to determine the factual claims becoming routine, the cost for the well-intentioned to get involved has become extremely high. There are at least some that are aware of significant security flaws that have opted to remain silent due to the highly toxic environment.
|
||||
|
||||
What was once driven by curiosity, a desire to learn and improve, to determine the viability of ideas, is now driven by blind greed, religious zealotry, self-righteousness, and self-aggrandizement.
|
||||
|
||||
I have precious little hope for the future of projects that inspire this type of zealotry, and its continuous spread will likely harm real research in this area for many years to come. These are technical projects, some projects succeed, some fail – this is how technology evolves. Those designing these systems are human, just as flawed as the rest of us, and so too are the projects flawed. Some are well suited to certain use cases and not others, some aren’t suited to any use case, none yet are suited to all. The discussions about these projects should be focused on the technical aspects, and done so to evolve this field of research; adding a religious to these projects harms all.
|
||||
|
||||
[Note: There are many examples of this behavior that could be cited, however in the interest of protecting those that have been targeted for criticizing projects, I have opted to minimize such examples. I have seen too many people who I respect, too many that I consider friends, being viciously attacked – I have no desire to draw attention to those attacks, and risk restarting them.]
|
||||
|
||||
|
||||
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
via: https://adamcaudill.com/2018/06/21/bitcoin-is-a-cult/
|
||||
|
||||
作者：[Adam Caudill][a]
|
||||
选题：[lujun9972](https://github.com/lujun9972)
|
||||
译者：[译者ID](https://github.com/译者ID)
|
||||
校对：[校对者ID](https://github.com/校对者ID)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
||||
|
||||
[a]:https://adamcaudill.com/author/adam/
|
||||
[1]:https://en.wikipedia.org/wiki/Merkle_tree
|
||||
[2]:https://hackernoon.com/how-crypto-kitties-disrupted-the-ethereum-network-845c22aa1e6e
|
||||
[3]:https://blockchain.info/block-height/528249
|
||||
[4]:https://en.wikipedia.org/wiki/An_Exceptionally_Simple_Theory_of_Everything
|
||||
[5]:https://medium.com/@coop__soup/00000000000000000021e800c1e8df51b22c1588e5a624bea17e9faa34b2dc4a-cd4b67d446be
|
||||
[6]:https://twitter.com/nondualrandy/status/1009609117768605696?ref_src=twsrc%5Etfw
|
||||
[7]:https://en.wikipedia.org/wiki/Numerology
|
||||
@ -1,3 +1,4 @@
|
||||
[tarepanda1024 翻译中]
|
||||
Three Graphical Clients for Git on Linux
|
||||
======
|
||||
|
||||
|
||||
74
translated/tech/20180516 How Graphics Cards Work.md
Normal file
74
translated/tech/20180516 How Graphics Cards Work.md
Normal file
@ -0,0 +1,74 @@
|
||||
显卡工作原理简介
|
||||
======
|
||||
![AMD-Polaris][1]
|
||||
|
||||
自从 sdfx 推出最初的 Voodoo 加速器以来，不起眼的显卡对你的 PC 是否可以玩游戏起到决定性作用，PC 上任何其它设备都无法与其相比。其它组件当然也很重要，但对于一个拥有 32GB 内存、价值 500 美金的 CPU 和 基于 PCIe 的存储设备的高端 PC，如果使用 10 年前的显卡，都无法以最高分辨率和细节质量运行当前<ruby>最高品质的游戏<rt>AAA titles</rt></ruby>，会发生卡顿甚至无响应。显卡（也常被称为 GPU, 或<ruby>图形处理单元<rt>Graphic Processing Unit</rt></ruby>）对游戏性能影响极大，我们反复强调这一点；但我们通常并不会深入了解显卡的工作原理。
|
||||
|
||||
出于实际考虑，本文将概述 GPU 的上层功能特性，内容包括 AMD 显卡、Nvidia 显卡、Intel 集成显卡以及 Intel 后续可能发布的独立显卡之间共同的部分。也应该适用于 Apple, Imagination Technologies, Qualcomm, ARM 和 其它显卡生产商发布的移动平台 GPU。
|
||||
|
||||
### 我们为何不使用 CPU 进行渲染？
|
||||
|
||||
我要说明的第一点是我们为何不直接使用 CPU 完成游戏中的渲染工作。坦率的说，在理论上你确实可以直接使用 CPU 完成<ruby>渲染<rt>rendering</rt></ruby>工作。在显卡没有广泛普及之前，早期的 3D 游戏就是完全基于 CPU 运行的，例如 Ultima Underworld（LCTT 译注：中文名为 _地下创世纪_ ，下文中简称 UU）。UU 是一个很特别的例子，原因如下：与 Doom （LCTT 译注：中文名 _毁灭战士_）相比，UU 具有一个更高级的渲染引擎，全面支持<ruby>向上或向下查找<rt>looking up and down</rt></ruby>以及一些在当时比较高级的特性，例如<ruby>纹理映射<rt>texture mapping</rt></ruby>。但为支持这些高级特性，需要付出高昂的代价，很少有人可以拥有真正能运行起 UU 的 PC。
|
||||
|
||||
|
||||
|
||||
对于早期的 3D 游戏，包括 Half Life 和 Quake II 在内的很多游戏，内部包含一个软件渲染器，让没有 3D 加速器的玩家也可以玩游戏。但现代游戏都弃用了这种方式，原因很简单：CPU 是设计用于通用任务的微处理器，意味着缺少 GPU 提供的<ruby>专用硬件<rt>specialized hardware</rt></ruby>和<ruby>功能<rt>capabilities</rt></ruby>。对于 18 年前使用软件渲染的那些游戏，当代 CPU 可以轻松胜任；但对于当代最高品质的游戏，除非明显降低<ruby>景象质量<rt>scene</rt></ruby>、分辨率和各种虚拟特效，否则现有的 CPU 都无法胜任。
|
||||
|
||||
### 什么是 GPU ?
|
||||
|
||||
GPU 是一种包含一系列专用硬件特性的设备，其中这些特性可以让各种 3D 引擎更好地执行代码，包括<ruby>形状构建<rt>geometry setup</rt></ruby>，纹理映射，<ruby>访存<rt>memory access</rt></ruby>和<ruby>着色器<rt>shaders</rt></ruby>等。3D 引擎的功能特性影响着设计者如何设计 GPU。可能有人还记得，AMD HD5000 系列使用 VLIW5 <ruby>架构<rt>archtecture</rt></ruby>；但在更高端的 HD 6000 系列中使用了 VLIW4 架构。通过 GCN （LCTT 译注：GCN 是 Graphics Core Next 的缩写，字面意思是下一代图形核心，既是若干代微体系结构的代号，也是指令集的名称），AMD 改变了并行化的实现方法，提高了每个时钟周期的有效性能。
|
||||
|
||||
|
||||
|
||||
Nvidia 在发布首款 GeForce 256 时（大致对应 Microsoft 推出 DirectX7 的时间点）提出了 GPU 这个术语，这款 GPU 支持在硬件上执行转换和<ruby>光照计算<rt>lighting calculation</rt></ruby>。将专用功能直接集成到硬件中是早期 GPU 的显著技术特点。很多专用功能还在（以一种极为不同的方式）使用，毕竟对于特定类型的工作任务，使用<ruby>片上<rt>on-chip</rt></ruby>专用计算资源明显比使用一组<ruby>可编程单元<rt>programmable cores</rt></ruby>要更加高效和快速。
|
||||
|
||||
GPU 和 CPU 的核心有很多差异，但我们可以按如下方式比较其上层特性。CPU 一般被设计成尽可能快速和高效的执行单线程代码。虽然 <ruby>同时多线程<rt>SMT, Simultaneous multithreading</rt></ruby> 或 <ruby>超线程<rt>Hyper-Threading</rt></ruby>在这方面有所改进，但我们实际上通过堆叠众多高效率的单线程核心来扩展多线程性能。AMD 的 32 核心/64 线程 Epyc CPU 已经是我们能买到的核心数最多的 CPU；相比而言，Nvidia 最低端的 Pascal GPU 都拥有 384 个核心。但相比 CPU 的核心，GPU 所谓的核心是处理能力低得多的的处理单元。
|
||||
|
||||
**注意：** 简单比较 GPU 核心数，无法比较或评估 AMD 与 Nvidia 的相对游戏性能。在同样 GPU 系列（例如 Nvidia 的 GeForce GTX 10 系列，或 AMD 的 RX 4xx 或 5xx 系列）的情况下，更高的 GPU 核心数往往意味着更高的性能。
|
||||
|
||||
你无法只根据核心数比较不同供应商或核心系列的 GPU 之间的性能，这是因为不同的架构对应的效率各不相同。与 CPU 不同，GPU 被设计用于并行计算。AMD 和 Nvidia 在结构上都划分为计算资源<ruby>块<rt>block</rt></ruby>。Nvidia 将这些块称之为<ruby>流处理器<rt>SM, Streaming Multiprocessor</rt></ruby>，而 AMD 则称之为<ruby>计算单元<rt>Compute Unit</rt></ruby>。
|
||||
|
||||
|
||||
|
||||
每个块都包含如下组件：一组核心，一个<ruby>调度器<rt>scheduler</rt></ruby>，一个<ruby>寄存器文件<rt>register file</rt></ruby>，指令缓存，纹理和 L1 缓存以及纹理<ruby>映射单元<rt>mapping units</rt></ruby>。SM/CU 可以被认为是 GPU 中最小的可工作块。SM/CU 没有涵盖全部的功能单元，例如视频解码引擎，实际在屏幕绘图所需的渲染输出，以及与<ruby>板载<rt>onboard</rt></ruby><ruby>显存<rt>VRAM, Video Memory</rt></ruby>通信相关的<ruby>内存接口<rt>memory interfaces</rt></ruby>都不在 SM/CU 的范围内；但当 AMD 提到一个 APU 拥有 8 或 11 个 Vega 计算单元时，所指的是（等价的）<ruby>硅晶块<rt>block of silicon</rt></ruby>数目。如果你查看任意一款 GPU 的模块设计图，你会发现图中 SM/CU 是反复出现很多次的部分。
|
||||
|
||||
|
||||
|
||||
GPU 中的 SM/CU 数目越多，每个时钟周期内可以并行完成的工作也越多。渲染是一种通常被认为是“高度并行”的计算问题，意味着随着核心数增加带来的可扩展性很高。
|
||||
|
||||
当我们讨论 GPU 设计时，我们通常会使用一种形如 4096:160:64 的格式，其中第一个数字代表核心数。在核心系列（如 GTX970/GTX 980/GTX 980 Ti, 如 RX 560/RX 580 等等）一致的情况下，核心数越高，GPU 也就相对更快。
|
||||
|
||||
### 纹理映射和渲染输出
|
||||
|
||||
GPU 的另外两个主要组件是纹理映射单元和渲染输出。设计中的纹理映射单元数目决定了最大的<ruby>纹素<rt>texel</rt></ruby>输出以及可以多快的处理并将纹理映射到对象上。早期的 3D 游戏很少用到纹理，这是因为绘制 3D 多边形形状的工作有较大的难度。纹理其实并不是 3D 游戏必须的，但不使用纹理的现代游戏屈指可数。
|
||||
|
||||
GPU 中的纹理映射单元数目用 4096:160:64 指标中的第二个数字表示。AMD，Nvidia 和 Intel 一般都等比例变更指标中的数字。换句话说，如果你找到一个指标为 4096:160:64 的 GPU，同系列中不会出现指标为 4096:320:64 的 GPU。纹理映射绝对有可能成为游戏的瓶颈，但产品系列中次高级别的 GPU 往往提供更多的核心和纹理映射单元（是否拥有更高的渲染输出单元取决于 GPU 系列和显卡的指标）。
|
||||
|
||||
<ruby>渲染输出单元<rt>Render outputs, ROPs</rt></ruby>（有时也叫做<ruby>光栅操作管道<rt>raster operations pipelines</rt></ruby>是 GPU 输出汇集成图像的场所，图像最终会在显示器或电视上呈现。渲染输出单元的数目乘以 GPU 的时钟频率决定了<ruby>像素填充速率<rt>pixel fill rate</rt></ruby>。渲染输出单元数目越多意味着可以同时输出的像素越多。渲染输出单元还处理<ruby>抗锯齿<rt>antialiasing</rt></ruby>，启用抗锯齿（尤其是<ruby>超级采样<rt>supersampled</rt></ruby>抗锯齿）会导致游戏填充速率受限。
|
||||
|
||||
### 显存带宽与显存容量
|
||||
|
||||
我们最后要讨论的是<ruby>显存带宽<rt>memory bandwidth</rt></ruby>和<ruby>显存容量<rt>memory capacity</rt></ruby>。显存带宽是指一秒时间内可以从 GPU 专用的显存缓冲区内拷贝进或拷贝出多少数据。很多高级视觉特效（以及更常见的高分辨率）需要更高的显存带宽，以便保证足够的<ruby>帧率<rt>frame rates</rt></ruby>，因为需要拷贝进和拷贝出 GPU 核心的数据总量增大了。
|
||||
|
||||
在某些情况下，显存带宽不足会成为 GPU 的显著瓶颈。以 Ryzen 5 2400G 为例的 AMD APU 就是严重带宽受限的，以至于提高 DDR4 的时钟频率可以显著提高整体性能。导致瓶颈的显存带宽阈值，也与游戏引擎和游戏使用的分辨率相关。
|
||||
|
||||
板载内存大小也是 GPU 的重要指标。如果按指定细节级别或分辨率运行所需的显存量超过了可用的资源量，游戏通常仍可以运行，但会使用 CPU 的主存存储额外的纹理数据；而从 DRAM 中提取数据比从板载显存中提取数据要慢得多。这会导致游戏在板载的快速访问内存池和系统内存中共同提取数据时出现明显的卡顿。
|
||||
|
||||
有一点我们需要留意，GPU 生产厂家通常为一款低端或中端 GPU 配置比通常更大的显存，这是他们为产品提价的一种常用手段。很难说大显存是否更具有吸引力，毕竟需要具体问题具体分析。大多数情况下，用更高的价格购买一款仅显存更高的显卡是不划算的。经验规律告诉我们，低端显卡遇到显存瓶颈之前就会碰到其它瓶颈。如果存在疑问，可以查看相关评论，例如 4G 版本或其它数目的版本是否性能超过 2G 版本。更多情况下，如果其它指标都相同，购买大显存版本并不值得。
|
||||
|
||||
查看我们的[极致技术讲解][2]系列，深入了解更多当前最热的技术话题。
|
||||
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
via: https://www.extremetech.com/gaming/269335-how-graphics-cards-work
|
||||
|
||||
作者：[Joel Hruska][a]
|
||||
选题：[lujun9972](https://github.com/lujun9972)
|
||||
译者：[pinewall](https://github.com/pinewall)
|
||||
校对：[校对者ID](https://github.com/校对者ID)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
||||
|
||||
[a]:https://www.extremetech.com/author/jhruska
|
||||
[1]:https://www.extremetech.com/wp-content/uploads/2016/07/AMD-Polaris-640x353.jpg
|
||||
[2]:http://www.extremetech.com/tag/extremetech-explains
|
||||
102
translated/tech/20180621 Bitcoin is a Cult - Adam Caudill.md
Normal file
102
translated/tech/20180621 Bitcoin is a Cult - Adam Caudill.md
Normal file
@ -0,0 +1,102 @@
|
||||
比特币是一个邪教 — Adam Caudill
|
||||
======
|
||||
经过这些年，比特币社区已经发生了非常大的变化；社区成员从闭着眼睛都能讲解 [Merkle 树][1] 的技术迷们，变成了被一夜爆富欲望驱使的投机者和由一些连什么是 Merkle 树都不懂的人所领导的企图寻求 10 亿美元估值的区块链初创公司。随着时间的流逝，围绕比特币和其它加密货币形成了一个狂热，他们认为比特币和其它加密货币远比实际的更重要；他们相信常见的货币（法定货币）正在成为过去，而加密货币将从根本上改变世界经济。
|
||||
|
||||
每一年他们的队伍都在壮大，而他们对加密货币的看法也在变得更加宏伟，那怕是因为[使用新技术][2]而使它陷入困境的情况下。虽然我坚信设计优良的加密货币可以使金钱的跨境流动更容易，并且在大规模通胀的领域提供一个更稳定的选择，但现实情况是，我们并没有做到这些。实际上，正是价值的巨大不稳定性才使得投机者赚钱。那些宣扬美元和欧元即将死去的人，已经完全抛弃了对现实世界客观公正的看法。
|
||||
|
||||
### 一点点背景 …
|
||||
|
||||
比特币发行那天，我读了它的白皮书 —— 它使用有趣的 [Merkle 树][1] 去创建一个公共账簿和一个非常合理的共识协议 —— 由于它新颖的特性引起了密码学领域中许多人的注意。在白皮书发布后的几年里，比特币变得非常有价值，并由此吸引了许多人将它视为是一种投资，和那些认为它将改变一切的忠实追随者（和发声者）。这篇文章将讨论的正是后者。
|
||||
|
||||
昨天，有人在推特上发布了一个最近的比特币区块的哈希，下面成千上万的推文和其它讨论让我相信，比特币已经跨越界线进入了真正的邪教领域。
|
||||
|
||||
一切都源于 Mark Wilcox 的这个推文：
|
||||
|
||||
> #00000000000000000021e800c1e8df51b22c1588e5a624bea17e9faa34b2dc4a
|
||||
> — Mark Wilcox (@mwilcox) June 19, 2018
|
||||
|
||||
张贴的这个值是 [比特币 #528249 号区块][3] 的哈希值。前导零是挖矿过程的结果；挖掘一个区块就是把区块内容与一个 nonce（和其它数据）组合起来，然后做哈希运算，并且它至少有一定数量的前导零才能被验证为有效区块。如果它不是正确的数字，你可以更换 nonce 再试。重复这个过程直到哈希值的前导零数量是正确的数字之后，你就有了一个有效的区块。让人们感到很兴奋的部分是接下来的 21e800。
|
||||
|
||||
一些人说这是一个有意义的编号，挖掘出这个区块的人实际上的难度远远超出当前所看到的，不仅要调整前导零的数量，还要匹配接下来的 24 位 —— 它要求非常强大的计算能力。如果有人能够以蛮力去实现它，这将表明有些事情很严重，比如，在计算或密码学方面的重大突破。
|
||||
|
||||
你一定会有疑问，为什么 21e800 如此重要 —— 一个你问了肯定会后悔的问题。有人说它是参考了 [E8 理论][4]（一个广受批评的提出标准场理论的论文），或是表示总共存在 2100000000 枚比特币（`21 x 10^8` 就是 2,100,000,000）。还有其它说法，因为太疯狂了而没有办法写出来。另一个重要的事实是，在前导零后面有 21e8 的区块平均每年被挖掘出一次 —— 这些从来没有人认为是很重要的。
|
||||
|
||||
这就引出了有趣的地方：关于这是如何发生的[理论][5]。
|
||||
|
||||
* 一台量子计算机，它能以某种方式用不可思议的速度做哈希运算。尽管在量子计算机的理论中还没有迹象表明它能够做这件事。哈希是量子计算机认为很安全的东西之一。
|
||||
* 时间旅行。是的，真的有人这么说，有人从未来穿梭回到现在去挖掘这个区块。我认为这种说法太荒谬了，都懒得去解释它为什么是错误的。
|
||||
* 中本聪回来了。尽管事实上他的私钥没有任何活动，一些人从理论上认为他回来了，他能做一些没人能做的事情。这些理论是无法解释他如何做到的。
|
||||
|
||||
|
||||
|
||||
> 因此，总的来说（按我的理解）中本聪，为了知道和计算他做的事情，根据现代科学，他可能是以下之一：
|
||||
>
|
||||
> A) 使用了一台量子计算机
|
||||
> B) 来自未来
|
||||
> C) 两者都是
|
||||
>
|
||||
> — Crypto Randy Marsh [REKT] (@nondualrandy) [June 21, 2018][6]
|
||||
|
||||
如果你觉得所有的这一切听起来像 [命理学][7]，不止你一个人是这样想的。
|
||||
|
||||
所有围绕有特殊意义的区块哈希的讨论，也引发了对在某种程度上比较有趣的东西的讨论。比特币的创世区块，它是第一个比特币区块，有一个不寻常的属性：早期的比特币要求哈希值的前 32 位是零；而创始区块的前导零有 43 位。因为由代码产生的创世区块从不会发布，它不知道它是如何产生的，也不知道是用什么类型的硬件产生的。中本聪有学术背景，因此可能他有比那个时候大学中常见设备更强大的计算能力。从这一点上说，只是对古怪的创世区块的历史有点好奇，仅此而已。
|
||||
|
||||
### 关于哈希运算的简单题外话
|
||||
|
||||
这种喧嚣始于比特币区块的哈希运算；因此理解哈希是什么很重要，并且要理解一个非常重要的属性，一个哈希是单向加密函数，它能够基于给定的数据创建一个伪随机输出。
|
||||
|
||||
这意味着什么呢？基于本文讨论的目的，对于每个给定的输入你将得到一个随机的输出。随机数有时看起来很有趣，很简单，因为它是随机的结果，并且人类大脑可以很容易从任何东西中找到顺序。当你从随机数据中开始查看顺序时，你就会发现有趣的事情 —— 这些东西毫无意义，因为它们只是简单地随机数。当人们把重要的意义归属到随机数据上时，它将告诉你很多这些参与者观念相关的东西，而不是数据本身。
|
||||
|
||||
### 币的邪教
|
||||
|
||||
首先，我们来定义一组术语：
|
||||
|
||||
* 邪教：一个宗教崇拜和直接向一个特定的人或物虔诚的体系。
|
||||
* 宗教：有人认为是至高无上的追求或兴趣。
|
||||
|
||||
|
||||
|
||||
币的狂热追捧者中的许多圣人，或许没有人比中本聪更伟大，他是比特币创始人的假名。强力的护卫、赋予能力和理解力远超过一般的研究人员，认为他的远见卓视无人能比，他影响了世界新经济的秩序。当将中本聪的神秘本质和未知的真实身份结合起来时，狂热的追随着们将中本聪视为一个真正的值的尊敬的人物。
|
||||
|
||||
当然，除了追随其他圣人的追捧者之外，毫无疑问这些追捧者认为自己是正确的。任何对他们的圣人的批评都被认为也是对他们的批评。例如，那些追捧 EOS 的人，可能会认为中本聪是开发了一个失败项目的黑客，而对 EOS 那怕是最轻微的批评，他们也会作出激烈的反应，之所以反应如此强烈，仅仅是因为攻击了他们心目中的神。那些追捧 IOTA 的人的反应也一样；还有更多这样的例子。
|
||||
|
||||
这些追随着在讨论问题时已经失去了理性和客观，他们的狂热遮盖了他们的视野。任何对这些项目和项目背后的人的讨论，如果不是溢美之词，必然以某种程序的刻薄言辞结束，对于一个技术的讨论那种做法是毫无道理的。
|
||||
|
||||
这很危险，原因很多：
|
||||
|
||||
* 开发者 & 研究者对缺陷视而不见。由于追捧者的大量赞美，这些参与开发的人对自己的能力开始膨胀，并将一些批评看作是无端的攻击 —— 因为他们认为自己是不可能错的。
|
||||
* 真正的问题是被攻击。技术问题不再被看作是需要去解决的问题和改进的机会，他们认为是来自那些想去破坏项目的人的攻击。
|
||||
* 用一枚币来控制他们。追随者们通常会结盟，而圣人仅有一个。承认其它项目的优越，意味着认同自己项目的缺陷或不足，而这是他们不愿意做的事情。
|
||||
* 阻止真实的进步。进化是很残酷的，它要求死亡，项目失败，以及承认这些失败的原因。如果忽视失败的教训，如果不允许那些应该去死亡的事情发生，进步就会停止。
|
||||
|
||||
|
||||
|
||||
许多围绕加密货币和相关区块链项目的讨论已经开始变得越来越”有毒“，善意的人想在不受攻击的情况下进行技术性讨论越来越不可能。随着对真正缺陷的讨论，那些在其它环境中注定要失败的缺陷，在没有做任何的事实分析的情况下即刻被判定为异端已经成为了惯例，善意的人参与其中的代价变得极其昂贵。至少有些人已经意识到极其严重的安全漏洞，由于高“毒性”的环境，他们选择保持沉默。
|
||||
|
||||
曾经被好奇、学习和改进的期望、创意可行性所驱动的东西，现在被盲目的贪婪、宗教般的狂热、自以为是和自我膨胀所驱动。
|
||||
|
||||
我对受这种狂热激励的项目的未来不抱太多的希望，而它持续地传播，可能会损害多年来在这个领域中真正的研究者。这些技术项目中，一些项目成功了，一些项目失败了 —— 这就是技术演进的方式。设计这些系统的人，就和你我一样都有缺点，同样这些项目也有缺陷。有些项目非常适合某些使用场景而不适合其它场景，有些项目不适合任何使用场景，没有一个项目适合所有使用场景。关于这些项目的讨论应该关注于技术方面，这样做是为了让这一研究领域得以发展；在这些项目中掺杂宗教般狂热必将损害所有人。
|
||||
|
||||
[注意：这种行为有许多例子可以引用，但是为了保护那些因批评项目而成为被攻击目标的人，我选择尽可能少的列出这种例子。我看到许多我很尊敬的人、许多我认为是朋友的人成为这种恶毒攻击的受害者 —— 我不想引起人们对这些攻击的注意和重新引起对他们的攻击。]
|
||||
|
||||
|
||||
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
via: https://adamcaudill.com/2018/06/21/bitcoin-is-a-cult/
|
||||
|
||||
作者：[Adam Caudill][a]
|
||||
选题：[lujun9972](https://github.com/lujun9972)
|
||||
译者：[qhwdw](https://github.com/qhwdw)
|
||||
校对：[校对者ID](https://github.com/校对者ID)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
||||
|
||||
[a]:https://adamcaudill.com/author/adam/
|
||||
[1]:https://en.wikipedia.org/wiki/Merkle_tree
|
||||
[2]:https://hackernoon.com/how-crypto-kitties-disrupted-the-ethereum-network-845c22aa1e6e
|
||||
[3]:https://blockchain.info/block-height/528249
|
||||
[4]:https://en.wikipedia.org/wiki/An_Exceptionally_Simple_Theory_of_Everything
|
||||
[5]:https://medium.com/@coop__soup/00000000000000000021e800c1e8df51b22c1588e5a624bea17e9faa34b2dc4a-cd4b67d446be
|
||||
[6]:https://twitter.com/nondualrandy/status/1009609117768605696?ref_src=twsrc%5Etfw
|
||||
[7]:https://en.wikipedia.org/wiki/Numerology
|
||||
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Reference in New Issue
Block a user