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Adobe Photoshop is the industry standard photo editing software, but it isn't the only way to give your images a new lease of life – there are plenty of free alternatives that put a huge array of powerful picture-enhancing tools at your fingertips. Simple photo-enhancing software has its place, but a genuine Photoshop alternative needs more than just red-eye correction and a handful of retro filters; it has to offer layers and masks, batch-editing, and a wide assortment of automatic and manual editing tools.
It also needs plugins to fill any gaps in its feature-set, and enable you to work as efficiently as possible. Some of Photoshop’s unique features (like asset-linking via Adobe Creative Cloud) mean it will always remain the professional’s tool of choice, but the rest of us have an excellent choice of free alternatives. Interface can be confusing Powerful and adaptable, GIMP is the best free Photoshop alternative. With layers, masks, advanced filters, color adjustment and transformations – all of which are fully customizable – its feature set it unbeatable. One of GIMP’s best features is its wealth of user-created plugins and scripts – many of which come pre-installed and ready to use.
Some of these replicate popular Photoshop tools (such as Liquify), and there’s a package of animation tools for bringing your photos to live via blending and morphing. If all that isn't enough,. If that all sounds a little intimidating, don’t worry – includes step-by-step tutorials and troubleshooting guides to get you started. The latest version of GIMP offers a new interface that puts all of its toolboxes, palettes and menus together in one window. This gives it a smart, Photoshop-like appearance, though its extensive patchwork of user-created tools means you’ll have to spend a little time experimenting and perusing the documentation to learn how to get the best results from each one. Photo Pos Pro. Size of exported files limited If you haven’t heard of Photo Pos Pro, you’re in for a treat.
This free Photoshop alternative aims to give the best of both worlds, offering interfaces for both novice and advanced users. The novice option puts one-click filters and automatic adjustments at the fore, while the latter closely resembles Photoshop.
Both are well designed, and more intuitive than GIMP’s endless lists and menus. Like Photoshop, Photo Pos Pro offers both layers and layer masks, as well as superb clone and healing brushes. All the expected color-refining tools are present and correct. There’s support for batch-editing and scripts to save time on routine tasks, you can import images directly from a scanner or camera.
Photo Pos Pro offers plugins in the form of extra frames and templates, and you can create and save your own filters for future use. Its main drawback is the limit on the size of saved files (1,024 x 2,014 pixels), but if you like the basic version and want to upgrade, Photo Pos Pro Premium is currently discounted to £17.67 (US$20, AU$30) – a very reasonable price for a top-rate Photoshop alternative. Less customizable than GIMP Open source Photoshop alternative Paint.NET started life as a substitute for Microsoft Paint, but over the years it’s grown into a powerful photo editor in its own right. Like GIMP and Photo Pos Pro, Paint.NET offers an excellent selection of automatic filters, plus manual editing tools for fine adjustments.
It also supports layers, though you’ll need to install a plugin for masks. Batch editing is included by default, and its clone stamp makes it easy to erase blemishes and distractions. Paint.NET isn’t quite as feature-filled as GIMP, but its smaller community of volunteer coders means its interface is more consistent and easier to use overall (though not as slick as Photo Pos Pro). Paint.NET is a particularly good Photoshop alternative for working with multiple photos thanks to quick-access tabs that use thumbnails to represent each open image at a glance.
It's also very fast, and runs well even on low-powered PCs. There’s no limit on the size of saved images, but it takes third place due to its smaller range of options and customizable tools. Pixlr Editor. May be deprecated soon is no ordinary free Photoshop alternative – it’s the work of AutoDesk, one of the biggest names in computer-aided design and 3D modelling software, and is as impressive as its pedigree implies. There are several versions available, including web, desktop and mobile apps.
Here we’re looking at the Pixlr Editor web app, which is the only one that supports layers. Pixlr Editor features a prominent ad on the right-hand side that limits the size of your working space but that’s its main drawback.
You get all the expected Photoshop-style tools (including sharpen, unsharp mask, blur, noise, levels and curves to name just a few), as well as artistic filters and automatic optimization options. Nothing is hidden behind a paywall. Pixlr Editor also gives you a toolbox very much like GIMP’s, with brushes, fills, selection, healing and clone stamp tools – all customizable via a ribbon above the workspace.
There’s support for both layers and masks, and although Pixlr Editor can't edit pictures in batches, it will cheerfully handle multiple images at once in different tabs. Sounds too good to be true? It might soon be., claiming that Flash “deserves everyone’s heartfelt salutation as it sails off into the sunset”.
Pixlr Editor is also built in Flash, but no HTML5 replacement has been announced, so we suspect that it might not be long for this world. For now, though, it’s a truly excellent Photoshop alternative – particularly if you don’t have the time or permission to download a desktop application. Adobe Photoshop Express. No plugin support is a lightweight version of the industry-standard photo editor available free for your browser, and as a downloadable app for Windows, iOS, and Android. Photoshop Express is the simplest of the tools here, but Adobe’s expertise in photo editing means it’s far superior to other quick-fix software. It packages Photoshop’s most useful picture-enhancing sleek, minimalist interface that’s particularly well suited to touchscreens. Sliders enable you to adjust contrast, exposure and white balance of your photo dynamically, and there are automatic options for one-click adjustments.
Once you’re satisfied with the results, you can either save the edited photo to your PC or share it via Facebook. The main appeal of Photoshop Express is its simplicity, but this is also its biggest drawback. There are no layers, plugins, or brush tools, and you can’t crop or resize your pictures. If you’re looking for a powerful image editor for your smartphone or tablet, Photoshop Fix (for restoring and correcting images) and Photoshop Mix (for combining and blending images) are also well worth investigating. Photoshop Mix even supports layers, and both apps integrate with Adobe’s Creative Cloud software, making it an excellent counterpart to the desktop version of Photoshop, as well as a superb tool in its own right. Working with video instead?
Check out our guide to the.
My last post elicited a comment from a C expert I was friends with long ago, recommending C as the language to replace C. Which ain’t gonna happen; if that were a viable future, Go and Rust would never have been conceived. But my readers deserve more than a bald assertion.
So here, for the record, is the story of why I don’t touch C any more. This is a launch point for a disquisition on the economics of computer-language design, why some truly unfortunate choices got made and baked into our infrastructure, and how we’re probably going to fix them. Along the way I will draw aside the veil from a rather basic mistake that people trying to see into the future of programming languages (including me) have been making since the 1980s. Only very recently do we have the field evidence to notice where we went wrong. I think I first picked up C because I needed GNU eqn to be able to output MathXML, and eqn was written in C. That project succeeded. Then I was a senior dev on Battle For Wesnoth for a number of years in the 2000s and got comfortable with the language.
Then came the day we discovered that a person we incautiously gave commit privileges to had fucked up the games’s AI core. It became apparent that I was the only dev on the team not too frightened of that code to go in. And I fixed it all right – took me two weeks of struggle. After which I swore a mighty oath never to go near C again. My problem with the language, starkly revealed by that adventure, is that it piles complexity on complexity upon chrome upon gingerbread in an attempt to address problems that cannot actually be solved because the foundational abstractions are leaky. It’s all very well to say “well, don’t do that” about things like bare pointers, and for small-scale single-developer projects (like my eqn upgrade) it is realistic to expect the discipline can be enforced. Not so on projects with larger scale or multiple devs at varying skill levels (the case I normally deal with).
With probability asymptotically approaching one over time and increasing LOC, someone is inadvertently going to poke through one of the leaks. At which point you have a bug which, because of over-layers of gnarly complexity such as STL, is much more difficult to characterize and fix than the equivalent defect in C. My Battle For Wesnoth experience rubbed my nose in this problem pretty hard. What works for a Steve Heller (my old friend and C advocate) doesn’t scale up when I’m dealing with multiple non-Steve-Hellers and might end up having to clean up their mess. So I just don’t go there any more.
Not worth the aggravation. C is flawed, but it does have one immensely valuable property that C didn’t keep – if you can mentally model the hardware it’s running on, you can easily see all the way down.
If C had actually eliminated C’s flaws (that it, been type-safe and memory-safe) giving away that transparency might be a trade worth making. As it is, nope. One way we can tell that C is not sufficient is to imagine an alternate world in which it is. In that world, older C projects would routinely up-migrate to C. Major OS kernels would be written in C, and existing kernel implementations like Linux would be upgrading to it.
In the real world, this ain’t happening. Not only has C failed to present enough of a value proposition to keep language designers uninterested in imagining languages like D, Go, and Rust, it has failed to displace its own ancestor. There’s no path forward from C without breaching its core assumptions; thus, the abstraction leaks won’t go away. Since I’ve mentioned D, I suppose this is also the point at which I should explain why I don’t see it as a serious contender to replace C. Yes, it was spun up eight years before Rust and nine years before Go – props to Walter Bright for having the vision. But in 2001 the example of Perl and Python had already been set – the window when a proprietary language could compete seriously with open source was already closing. The wrestling match between the official D library/runtime and Tango hurt it, too.
It has never recovered from those mistakes. So now there’s Go (I’d say “and Rust”, but for reasons I’ve discussed before I think it will be years before Rust is fully competitive). It is type-safe and memory-safe (well, almost; you can partway escape using interfaces, but it’s not normal to have to go to the unsafe places). One of my regulars, Mark Atwood, has correctly pointed out that Go is a language made of grumpy-old-man rage, specifically rage by one of the designers of C (Ken Thompson) at the bloated mess that C became. I can relate to Ken’s grumpiness; I’ve been muttering for decades that C attacked the wrong problem.
There were two directions a successor language to C might have gone. One was to do what C did – accept C’s leaky abstractions, bare pointers and all, for backward compatibility, than try to build a state-of-the-art language on top of them. The other would have been to attack C’s problems at their root – fix the leaky abstractions.
That would break backward compatibility, but it would foreclose the class of problems that dominate C/C defects. The first serious attempt at the second path was Java in 1995. It wasn’t a bad try, but the choice to build it over a j-code interpreter mode it unsuitable for systems programming. That left a huge hole in the options for systems programming that wouldn’t be properly addressed for another 15 years, until Rust and Go. In particular, it’s why software like my GPSD and NTPsec projects is still predominantly written in C in 2017 despite C’s manifest problems. This is in many ways a bad situation. It was hard to really see this because of the lack of viable alternatives, but C/C has not scaled well.
Most of us take for granted the escalating rate of defects and security compromises in infrastructure software without really thinking about how much of that is due to really fundamental language problems like buffer-overrun vulnerabilities. So, why did it take so long to address that? It was 37 years from C (1972) to Go (2009); Rust only launched a year sooner.
I think the underlying reasons are economic. Ever since the very earliest computer languages it’s been understood that every language design embodies an assertion about the relative value of programmer time vs. Machine resources. At one end of that spectrum you have languages like assembler and (later) C that are designed to extract maximum performance at the cost of also pessimizing developer time and costs; at the other, languages like Lisp and (later) Python that try to automate away as much housekeeping detail as possible, at the cost of pessimizing machine performance. In broadest terms, the most important discriminator between the ends of this spectrum is the presence or absence of automatic memory management. This corresponds exactly to the empirical observation that memory-management bugs are by far the most common class of defects in machine-centric languages that require programmers to manage that resource by hand.
A language becomes economically viable where and when its relative-value assertion matches the actual cost drivers of some particular area of software development. Language designers respond to the conditions around them by inventing languages that are a better fit for present or near-future conditions than the languages they have available to use.
Over time, there’s been a gradual shift from languages that require manual memory management to languages with automatic memory management and garbage collection (GC). This shift corresponds to the Moore’s Law effect of decreasing hardware costs making programmer time relatively more expensive. But there are at least two other relevant dimensions.
One is distance from the bare metal. Inefficiency low in the software stack (kernels and service code) ripples multiplicatively up the stack. This, we see machine-centric languages down low and programmer-centric languages higher up, most often in user-facing software that only has to respond at human speed (time scale 0.1 sec). Another is project scale.
Every language also has an expected rate of induced defects per thousand lines of code due to programmers tripping over leaks and flaws in its abstractions. This rate runs higher in machine-centric languages, much lower in programmer-centric ones with GC.
As project scale goes up, therefore, languages with GC become more and more important as a strategy against unacceptable defect rates. When we view language deployments along these three dimensions, the observed pattern today – C down below, an increasing gallimaufry of languages with GC above – almost makes sense. But there is something else going on. C is stickier than it ought to be, and used way further up the stack than actually makes sense. Why do I say this?
Consider the classic Unix command-line utilities. These are generally pretty small programs that would run acceptably fast implemented in a scripting language with a full POSIX binding. Re-coded that way they would be vastly easier to debug, maintain and extend. Why are these still in C (or, in unusual exceptions like eqn, in C)? Transition costs. It’s difficult to translate even small, simple programs between languages and verify that you have faithfully preserved all non-error behaviors.
More generally, any area of applications or systems programming can stay stuck to a language well after the tradeoff that language embodies is actually obsolete. Here’s where I get to the big mistake I and other prognosticators made. We thought falling machine-resource costs – increasing the relative cost of programmer-hours – would be enough by themselves to displace C (and non-GC languages generally). In this we were not entirely or even mostly wrong – the rise of scripting languages, Java, and things like Node.js since the early 1990s was pretty obviously driven that way. Not so the new wave of contending systems-programming languages, though. Rust and Go are both explicitly responses to increasing project scale. Where scripting languages got started as an effective way to write small programs and gradually scaled up, Rust and Go were positioned from the start as ways to reduce defect rates in really large projects.
Like, Google’s search service and Facebook’s real-time-chat multiplexer. I think this is the answer to the “why not sooner” question. Rust and Go aren’t actually late at all, they’re relatively prompt responses to a cost driver that was underweighted until recently. OK, so much for theory. What predictions does this one generate? What does it tell us about what comes after C?
Here’s the big one. The largest trend driving development towards GC languages haven’t reversed, and there’s no reason to expect it will. Therefore: eventually we will have GC techniques with low enough latency overhead to be usable in kernels and low-level firmware, and those will ship in language implementations. Those are the languages that will truly end C’s long reign. There are broad hints in the working papers from the Go development group that they’re headed in this direction – references to academic work on concurrent garbage collectors that never have stop-the-world pauses. If Go itself doesn’t pick up this option, other language designers will. But I think they will – the business case for Google to push them there is obvious (can you say “Android development”?).
Well before we get to GC that good, I’m putting my bet on Go to replace C anywhere that the GC it has now is affordable – which means not just applications but most systems work outside of kernels and embedded. The reason is simple: there is no path out of C’s defect rates with lower transition costs. I’ve been experimenting with moving C code to Go over the last week, and I’m noticing two things. One is that it’s easy to do – C’s idioms map over pretty well. The other is that the resulting code is much simpler.
One would expect that, with GC in the language and maps as a first-class data type, but I’m seeing larger reductions in code volume than initially expected – about 2:1, similar to what I see when moving C code to Python. Sorry, Rustaceans – you’ve got a plausible future in kernels and deep firmware, but too many strikes against you to beat Go over most of C’s range. No GC, plus Rust is a harder transition from C because of the borrow checker, plus the standardized part of the API is still seriously incomplete (where’s my select(2), again?).
The only consolation you get, if it is one, is that the C fans are screwed worse than you are. At least Rust has a real prospect of dramatically lowering downstream defect rates relative to C anywhere it’s not crowded out by Go; C doesn’t have that. An orthogonal problem is the type system: in most object-derived systems, there is a complex type system with at least single inheritance. This leads to an error a former customer made: we /completely/ modelled a tournament in the form of a type hierarchy. When you wanted to change it, you had to change everything.
When you wanted to add something new, you had to apply it to everything. We re-invented spagetti code, only this time it was spagetti data structures. Instead of abstracting and simplifying, we made it more complex. At that point I am not even sure what is the point of OOP. Since SQL tables as a single “type” are useful for a tremendous range of purposes, while I never tried systems programming, if I would try I would probably use any “list of thingies with named properties” idiom that comes my way, be that a hash table or a struct. OOP was pretty much.invented. for inheritance, at least the ways I was taught at school, these awesome chains of concepts that a BMW inherits from Car and Car from Vehicle, i.e.
Basically what David described was taught as good design at my school but if it is not, then why even bother? I take an SQL table or the language equivalent thereof, hashtable, struct, whatever, name it Car, some of the fields will be Make and Model and call it a day. Essentially I have to find the sweet spot in the conceptual category-subcategory tree, which in this case is car.
Inheritance was meant to be able to move up and down on this tree, but the tree is not cast in stone because the Chevrolet company can acquire Daewoo and next week the Daewoo Matiz is called Chevrolet Matiz, then I am sure as hell not having any object class called BMW: that will be data, easily changed not part of the data structure! Encapsulation is a better idea but unless I have massive, database-like data structures (which in real life I always do but system programmers maybe not), how am I going to automatically test any function that works not only with its own parameters but pretty much everything else it can find inside the same object? I mean great, objects cut down global variable hell to a protected variable minihell that is far easier to eyeball but is it good enough for automated testing? I am afraid to write things like this, because only a narrow subset of my profession involves writing code and as such I am not a very experienced programmer so I should not really argue with major CS concepts.
Still for example Steve Yegge had precisely this beef with OOP: you are writing software yet OOP really seems to want you make you want to make something like unchangeable, fixed, cast in stone hardware. OOP was hugely hyped, especially in the corporate world by Java marketers, whom extolled the virtues of how OOP and Java would solve all their business problems. As it turns out, POP (protocol-oriented programming) is the better design, and so all modern languages are using it. POP’s critical feature is generics, so it’s baffling as to why Go does not have generics. Basically, rather than separating structures into class hierarchies, you assign shared traits to structures in a flat hierarchy.
You can then pull out trait-based generics to execute some rather fantastical solutions that would otherwise require an incredible degree of copying and pasting (a la Go). This then allows you to interchangeably use a wide variety of types as inputs and fields into these generic functions and structures, in a manner that’s very efficient due to adhering to data-oriented design practices.
It’s incredibly useful when designing entity-component system architectures, where components are individual pieces of data that are stored in a map elsewhere; entities consist of multiple components (but rather than owning their components directly, they hold ID’s to their components), and are able to communicate with other entities; and systems, which are the traits that are implemented on each entity that is within the world map. Enables for some incredible flexibility and massively parallel solutions, from UIs to game engines. Entities can have completely different types, but the programmer does not need to be aware of that, because they all implement the same traits, and so they can interact with each other via their trait impls. And in structuring your software architecture in this way, you ensure that specific components are only ever mutably borrowed when it is needed, and thus you can borrow many components and apply systems to them in parallel. I prefer go, but Nim is pretty neat.
And now it turns out that a guy who tried writing a Unix kernel in Rust This surprises me. I had bought into the pro-Rust argument that, once it matured, would be a good match for kernel development on theoretical grounds related to the tradeoff between the benefits of type safety versus the additional friction costs of Rust. Now someone actually working at that coal face says “Nope.” More detail I suppose it’s possible that sticking to Rust would have been a better choice and that the guy is just incompetent, but his discussion of the issues seems pretty thoughtful.
Not actually. I have read many success stories from newcomers to programming.
Even some which had tried to pick up programming several times in the past with C and C. Rust was able to take the cake because of it’s superior degree of documentation; a better, explicit syntax; an informative compiler that points out the mistakes, including the borrow checker, which helpfully points out memory unsafety; and a very vibrant and friendly community that’s always standing by to help newcomers get started and to demonstrate idiomatic Rust. The problem with ESR, on the other hand, is that he never attempted to reach for any of these resources when he tried out Rust. I never saw him make a post in Reddit, the Rust users forum, or visit any IRC/Mattermost channels. He simply wrote a post a misinformed post about Rust because he was doing something he didn’t understand, and wasn’t aware that what he thought was missing in the standard library was actually there. Even I, myself, come from a background of never having programmed anything before Rust. And yet Rust was the perfect entry into programming.
I can now write quality C, C, etc. Because the rules enforced by Rust are the best practices in those languages. And I can now do everything from writing kernels and system shells to full stack web development and desktop GUI applications — all with Rust.
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All the rules in Rust are intuitive and instinctual for me today. Honestly, I never got frustrated. I have a general philosophy that if you struggle with something, it’s probably because you’re going about it the wrong way, and that you should instead take a step back and review. In addition, if you are having difficulty changing your perspective to figure out what you’re doing wrong, you’re free to reach out to the greater online community, where armies of other developers ahead of you are eager to answer your questions. As it turns out, Rust’s borrow checker is like a Chinese finger trap — the more you resist it, the more you will struggle. If you instead go with the flow and internalize the rules, the struggles disappear, and the solutions become apparent. Everything suddenly makes sense when you simply accept the rules, rather than trying to fight the rules.
I initially struggled to wrap my mind around all the new concepts during the first week, but by the end of the second week, all of the concepts were well in-grained within my mind: what move semantics are and how they work, the borrowing and ownership model, sum types and pattern matching, traits and generics, mutexes and atomics, iterators and map/fold/filter/etc. And that’s talking about the state of documentation that was really poor when I initially picked up Rust. Rust of today has significantly enhanced documentation that covers every area, and does so better than any other language I’ve ever seen. If I had that to reference when I started, then I’m sure that I could have mastered it within a week. After learning Rust, I found that I could easily write C and C as well, because they were more or less ancient history in terms of systems language concepts.
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The rules enforced by the Rust compiler are best practices in C/C. It’s just annoying how much boiler plate you need in those languages to achieve simple tasks that Rust’s standard library already encompasses, and how the core language is so critically lacking that you have to attempt to emulate sum types by hand. Honestly, after 2 years of Rust, I often get frustrated at Go not providing the same safety and convenience. I may be spending hours trying to make the borrow checker happy with my data usage, by I regularly spend days trying to debug segmentation faults in Go My point is that “instinctive” depends heavily on what you are used to use. Go might be “instinctive” when you come form C, and Rust might be too different from common languages to be instinctive at all, but once you get used to it, you wish you never have to turn back.
Microkernels like Zircon are.exactly. the place where C/C will likely remain a reasonable choice, if not the best choice, for years to come. The primary requirements are performance and complete access to the bare metal. A true microkernel has a small code base (or it isn’t a.micro. kernel!), so it isn’t a “programming in the large” situation. A small team working on a small code base.can. maintain the discipline to use C effectively and avoid its pitfalls.
On the other hand, the various services built on top of Zircon are free to use other languages. Many are in C now, but they don’t have to be.
The FAT filesystem service is written in go. Another bit is the unix/posix call set – open/close/read/write/ioctl – reinvented badly many times. Never improved.
Where is the support for overlapped (asynchronous) I/O in the base POSIX call set? Answer: There is none. Sure, there’s an AIO extension to POSIX that no one uses, that is completely inadequate when compared to a kernel such as Windows that has support for async I/O with sophisticated control features like I/O completion ports designed in, and that is implemented under Linux by spinning off user-space worker threads.
Since completion-based AIO is a first-class citizen under Windows, you can set up overlapped I/O operations — to network, devices, or disk — to notify you upon their completion and then busy your CPU cores with other tasks, rather than the POSIX model of spinning in select loops and interleaving “are we there yet? Are we there yet?” polling with other processing.
So yes, the POSIX model has been improved on. You know that old Dilbert cartoon where the Unix guy says “Here’s a quarter, get yourself a real operating system”? Dave Cutler — lead designer of VMS and Windows NT — does that to Unix guys. There have been entire papers written on that. It’s not that “UNIXy OSes” are inferior to Windows, it’s that the standards organizations are derelict in their duty to provide a portable API in the style that everyone actually wants to use. Be it Linux, FreeBSD, OSX, or what have you, there ARE heavily-used equivalents to the Windows APIs you mention in POSIXy OSes they’re just all different.
(I say UNIXy and POSIXy because it’s intentional that Linux aims to be “certifiable but not officially certified” due to its rapid release cycle.). I find myself thinking that said small embedded systems are, in a way, echoes of the minicomputers that C was originally made to run on. I say echoes, because while I’m pretty sure a PDP-11 had more raw compute power and I/O throughput due to its architecture, the memory numbers seem similar.
While I read that a PDP-11 could be configured with up to 4 MiB of core and multiple disks, I doubt a majority of them were delivered or later configured to be fully maxed out. And when I look up the PDP-11, I read that a great many of them were employed in the same job that today’s embedded systems are: as real-time automated control systems.
Being a whippersnapper who wasn’t born until at least a decade later, I may well be overgeneralizing, but I don’t think I’m completely wrong either. So, when considering that, it makes sense that the aforementioned niche is where C is likely to hold out the longest. It’s a similar environment to the one it was originally adapted to. I’m pretty sure a PDP-11 had more raw compute power and I/O throughput due to its architecture, the memory numbers seem similar. While today’s proficient embedded programmer would be right at home with the PDP-11, and while this statement holds true for some embedded systems, it’s certainly not true for all. Moore’s law has done some interesting things for us.
Package pins are costly, so for a lot of small embedded systems, it may make more sense to have all the memory on-chip. Once you have all the memory on-chip, you don’t want too much of it, or your cost goes up again because of the die size. Performance costs are somewhat orthogonal to memory costs: performance increases by adding more transistors and by reducing feature size.
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Both of these are expensive, but not always as expensive as adding memory. One cool thing about on-chip memory is that since you’re not constrained by pins for an external bus, you can have a really wide bus to memory. Another cool thing is that you can have interleaved buses if you want, simply by splitting memories up in odd ways. Interleaving buses allows for simultaneous access from a CPU to one word with DMA to an adjacent word.
So there are a lot of niche devices that fit in this C niche, in fact that are small enough to not even want to run an OS on, never mind an interpreter or garbage collector — that are nonetheless performant enough to, for example, saturate a few full-duplex gigabit ethernet links while doing complex DSP calculations. In other words, a $5.00 chip might very well exceed a PDP-11 by orders of magnitude in memory bandwidth, CPU power, and I/O bandwidth. I agree about Moore’s law on small embedded systems, or more narrowly, on their CPU’s. I sometimes write code for these things – in C – because a customer uses them for their very low cost. The processors are a little faster than in the 1980’s, and they’ve added a few instructions, but basically, a PIC18 is still a tiny little machine with a horrible machine model, just like its 1980’s progenitor. A 68HC05 is still a 6805 which is just a bit more than a 6800, from the 1970’s.
However, Moore’s law does appear – the greatly increased chip density leads to very chap SoC’s – a dollar for a machine with RAM, Flash, EEPROM, and a whole bucket of built in peripherals and peripheral controllers. The good news is that you can indeed use C on these things, rather than assembly (which is true torture on a PIC). And, the C optimizes pretty well.
@esr: The first serious attempt at the second path was Java in 1995. It wasn’t a bad try, but the choice to build it over a j-code interpreter mode it unsuitable for systems programming. I agree Java was a poor choice for systems programming, but I don’t think it was ever intended to be a systems programming language. The goal of Java was “Write once, run anywhere”. Java code compiled to bytecode targeting a virtual CPU, and the code would actually be executed by the JRE.
If your hardware could run a full JRE, the JRE handled the abstraction away from the underlying hardware and your code could run. The goal was cross-platform, because the bytecode was the same regardless of what system it was compiled on.
(I have IBM’s open source Eclipse IDE here. The same binary runs on both Windows and Linux.) For applications programming, that was a major win. (And unless I’m completely out of touch, the same comments apply to Python.) More generally, any area of applications or systems programming can stay stuck to a language well after the tradeoff that language embodies is actually obsolete. Which is why there are probably billions of lines of COBOL still in production. It’s just too expensive to replace, regardless of how attractive the idea is. But I think they will – the business case for Google to push them there is obvious (can you say “Android development”?).
Android has a Linux kernel, but most stuff running on Android is written in Java and executed by the Dalvik JRE. The really disruptive change might be if Google either rewrote the Linux kernel in Go, or wrote a completely new kernel intended to look and act like Linux in Go. The question is whether Linux’s days are numbered in consequence. Such tuning would not be required It’s an “edge case”.
I really don’t understand the obsession with GC languages. In comparison to GC-free Rust. You’re an odd one to speak of obsessions.
Why pay for GC when you don’t even need it? Why pay for manual memory management when you don’t even need it? See how it is to write un-proveable assertions. Even I can do it. Rust appears to have a lot to offer.
It behooves all of us to get to know it. The biggest impediment to Rust’s adoption is the people promoting it. It’s an “edge case”. I spent two years experimenting with Go, and I can tell you that tuning the GC is not an edge case. It’s very common. Why pay for manual memory management when you don’t even need it?
See how it is to write un-proveable assertions. Even I can do it. You aren’t paying for manual memory management.
Rust’s compiler and language does all of that for you. You’re trying to argue against an absolute. What a shame. Either you pay hardware costs to implement a solution, or you create a simpler solution that doesn’t need to pay those costs. It’s obvious which of the two are better options!
The biggest impediment to Rust’s adoption is the people promoting it. Purely false. Rust has a healthy adoption rate. It arrived at precisely the right time when it did, to take advantage of all the concepts and theories that had been developed at the time it started development, and has been adopted at precisely the correct rate, to enable the Crates ecosystem to catch up to the needs of the developers adopting it. Rust’s community is growing exponentially, regardless of how much you snarl your nose at it. It doesn’t matter what you or I say. Any publicity, is good publicity!
Rust appears to have a lot to offer. It behooves all of us to get to know it. It does nothing of the sort.
It is simply the correct tool for the biggest problem in the software industry. Either you choose to use it of your own volition, or you fall behind into obscurity, and a new generation of software developers replaces you. You’re an odd one to speak of obsessions.
In all fairness, the advantages of Rust’s approach to memory allocation and deallocation predate Rust itself, with antecedents in C and even Objective-C. Rust merely builds on and enhances these things. But there is an inherent cost to runtime garbage collection that simply is not paid when your language determines object lifetime at compile time. Tracing GCs are, in a word, obsolete: 1960s technology does not fare well in the face of 2010s problems of scalability and performance.
Rust earned its position as the prime candidate to replace C as the default systems language in two ways: by not adopting a GC and not sucking as bad as C. Three ways, actually, if you count being hipster-compliant (which Ada is not).