I was reading this article. The author talks about "The Blub Paradox". He says programming languages vary in power. That makes sense to me. For example, Python is more powerful than C/C++. But its performance is not as good as that of C/C++.

Is it always true that more powerful languages must necessarily have lesser possible performance when compared to less powerful languages? Is there a law/theory for this?

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    $\begingroup$ The definition of "powerful" is apparently quite subjective. I've always considered C and C++ to be much more powerful than Python because it gives you lower level access to what happens in your system. In Python you're much more limited by functionality the language makes available to you. It's pretty telling that some Python libraries are written in C. Python might be easier to code in and more concise and expressive, but that's not the same as being powerful. A grenade might be easier to make and wield than an atom bomb, but I don't think anyone would argue it's a more powerful weapon. $\endgroup$ – Bernhard Barker Jun 21 at 18:00
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    $\begingroup$ The reason some Python libraries are written in C is that for many, many years, the only available Python implementation had abysmal performance. They are written in C by necessity, not by choice. Compare e.g. to the PyPy Python implementation, where those same libraries are written in either Python or RPython. $\endgroup$ – Jörg W Mittag Jun 21 at 18:28
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    $\begingroup$ @rcgldr: But at least with C++, code written for supercomputers tends to avoid language constructs that lead to slow code. I've certainly improved a few such programs by converting C++ stuff to C, and recompiling with the C++ compiler. $\endgroup$ – jamesqf Jun 22 at 3:06
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    $\begingroup$ I saw an interview once with Charles Oliver Nutter, the lead developer of JRuby. In this interview, he mentioned that he met the team at Oracle that was responsible for one sub-component of one of the three garbage collectors of the Oracle HotSpot JVM, and he realized that this team was bigger than all core teams of all Ruby implementations combined. That is what influences performance the most: how much you invest in performance. This should be a tautology, but for some reason is still surprising to many people: if you invest in performance, you get performance. $\endgroup$ – Jörg W Mittag Jun 22 at 8:23
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    $\begingroup$ Saying Python is more powerful than C/C++ is a bit like watching someone heat up a frozen TV dinner in nine minutes and thinking that they've somehow outperformed a Michelin starred chef. Because... quick and easy? $\endgroup$ – J... Jun 23 at 11:56

TL;DR: Performance is a factor of Mechanical Sympathy and Doing Less. Less flexible languages are generally doing less and being more mechanically sympathetic, hence they generally perform better out of the box.

Physics Matter

As Jorg mentioned, CPU designs today co-evolved with C. It's especially telling for the x86 instruction set which features SSE instructions specifically tailored for NUL-terminated strings.

Other CPUs could be tailored for other languages, and that may give an edge to such other languages, but regardless of the instruction set there are some hard physics constraints:

  • The size of transistors. The latest CPUs feature 7nm, with 5nm being experimental. Size immediately places an upper bound on density.
  • The speed of light, or rather the speed of electricity in the medium, places on an upper bound on the speed of transmission of information.

Combining the two places an upper bound on the size of L1 caches, in the absence of 3D designs -- which suffer from heat issues.

Mechanical Sympathy is the concept of designing software with hardware/platform constraints in mind, and essentially to play to the platform's strengths. Language Implementations with better Mechanical Sympathy will outperform those with lesser Mechanical Sympathy on a given platform.

A critical constraint today is being cache-friendly, notably keeping the working set in the L1 cache, and typically GCed languages use more memory (and more indirections) compared to languages where memory is manually managed.

Less (Work) is More (Performance)

There's no better optimization than removing work.

A typical example is accessing a property:

  • In C value->name is a single instruction (lea).
  • In Python or Ruby, the same typically involves a hash table lookup.

The lua instruction is executed in 1 CPU cycle, an optimized hash table lookup takes at least 10 cycles.

Recovering performance

Optimizers, and JIT optimizers, attempt to recover the performance left on the table.

I'll take the example of two typical optimizations for JavaScript code:

  • NaN-tagging is used to store a double OR a pointer in 8 bytes. At run-time, a check is performed to know which is which. This avoids boxing doubles, eliminating a separate memory allocation and an indirection, and thus is cache-friendly.
  • The V8 VM optimizes dynamic property lookups by creating a C-like struct for each combination of properties on an object, hence going from hash table lookup to type-check + lea -- and possibly lifting the type-check much earlier.

Thus, to some extent, even highly flexible languages can be executed efficiently... so long as the optimizer is smart enough, or the developer makes sure to massage the code to just hit the optimizer's sweet spot.

There is no faster language...

... there are just languages that are easier to write fast programs in.

I'll point to a serie of 3 blog articles from 2018:

I think the latter article is the key point. More flexible languages can be made to run efficiently with expert's knowledge, and time. This is costly, and typically brittle.

The main advantage of less flexible languages -- statically typed, tighter control on memory -- are that they make optimizing their performance more straightforward.

When the language's semantics already closely match the platform sweet spot, good performance is straight out of the box.

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This is simply not true. And part of why it's false is that the premise isn't well formed.

There is no such thing as a fast or slow language. The expressive power of a language is purely a function of its semantics. It is independent of any particular implementation.

You can talk about the performance of code generated by GCC, or about the performance of the CPython interpreter. But these are specific implementations of the language. You could write a very slow C compiler, and you can write Python interpreters that are quite fast (like PyPy).

So the answer to the question of "is more power necessarily slower" is no, purely because you or I can go write a slow C compiler, that has the same expressive power as GCC, but that is slower than Python.

The real question is "why do more powerful languages tend to have slower implementations." The reason is that, if you're considering the C vs Python, the difference in power is abstraction. When you do something in Python, there is a lot more that is implicit that is happening behind the scenes. More stuff to do means more time.

But there's also lots of social elements at play. People who need high performance choose low level languages, so they have fine grained control of what the machine is doing. This has led to the idea that low level languages are faster. But for most people, writing in C vs Python will have pretty comparable performance, because most applications don't require that you eke out every last millisecond. This is particularly true when you consider the extra checks that are manually added to program defensively in C. So just because lots of specialists have built fast things in C and C++ doesn't mean they're faster for everything.

Finally, some languages have zero cost abstraction. Rust does this, using a type system to ensure memory safety without needing runtime garbage collection. And Go has garbage collection, but it's so fast that you get performance on par with C while still getting extra power.

The TLDR is that more powerful languages are sometimes faster in some cases, but this is not a firm rule, and there are exceptions and complications.

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    $\begingroup$ First you say "this is simply not true", and then you say "when you do something in Python, there is a lot more that is implicit that is happening behind the scenes", which flatly contradicts the first statement. It is true, and you are providing the correct reason for it. Yes, there are fast and slow languages, as you clearly demonstrate. Why you come to the opposite conclusion is beyond me. $\endgroup$ – Peter - Reinstate Monica Jun 22 at 7:38
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    $\begingroup$ @Peter-ReinstateMonica The point is that a language by itself cannot be "fast" or "slow". It's its implementation, its compiler or interpreter, that will produce code that runs faster or slower. Even C, with the same compiler, can itself produce slower of faster executions depending on the optimization. Which one is the speed of the language? You can have languages easier to implement faster because they are less abstract, but nothing prohibit you to implement an abstract language so that you produce really fast execution. $\endgroup$ – bracco23 Jun 22 at 9:55
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    $\begingroup$ @bracco23 Re "nothing prohibit you to implement an abstract language so that you produce really fast execution": jmite actually gave a good reason which prevents it ("here is a lot more that is implicit that is happening behind the scenes"). It's inherent in languages which e.g. carry run time information with their types etc. (even in C++ RTTI carries a small cost). $\endgroup$ – Peter - Reinstate Monica Jun 22 at 11:58
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    $\begingroup$ @Bergi That's wrong though: an optimal Python implementation (given today's machines and software engineering state of the art) will be slower than an optimal C++ implementation. The Python implementation simply has more work to do at runtime, and there's only so much we can do to optimise it. $\endgroup$ – Konrad Rudolph Jun 22 at 19:57
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    $\begingroup$ I have to disagree with this answer, and my view is similar to Peter's. There is such a thing as a slow language, a language that cannot be implemented efficiently. For example, I can say that my language contains a primitive operation that is the busy beaver function, or to be less extreme some function that has very bad time complexity. Languages specify behavior, but if that behavior cannot be implemented efficiently then there will be no efficient implementation of the language. JS engines today are a great example of optimized implementations of an inefficient language. $\endgroup$ – Mario Carneiro Jun 23 at 4:25

Is it always true that more powerful languages must necessarily have lesser possible performance when compared to their less powerful counterparts? Is there a law/theory for this?

First off, we need to make one thing clear: languages don't have "performance".

A particular program written in a particular programming language executed on a particular machine in a particular environment under particular conditions using a particular version of a particular implementation of the programming language has a particular performance. This does not mean that all programs written in that language have a particular performance.

The performance that you can attain with a particular implementation is mostly a function of how many resources, how much money, how many engineers, etc. are invested to make that implementation fast. And the simple truth is that C compilers have more money and more resources invested in them than Python implementations. However, that does not mean that a Python implementation cannot be fast. A typical Python implementation has about as many full-time engineers as a typical C compiler vendor has full-time custodians that re-fill the developers' coffee machines.

Personally, I am more familiar with the Ruby community, so I will give some examples from there.

The Hash class (Ruby's equivalent to Python's dict) is written in 100% C in YARV. In Rubinius, however, it is written (mostly) in Ruby (relying only on a Tuple class that is partially implemented using VM primitives).

The performance of Hash-intensive benchmarks running on Rubinius is not significantly worse than running on YARV, which means that at least for those particular combinations of benchmark, language, operating system, CPU, environment, load, etc. Ruby is about as fast as C.

Another example is TruffleRuby. The TruffleRuby developers set up an interesting benchmark: they found two Ruby libraries that use lots Ruby idioms that are thought to be notoriously hard to optimize, such as runtime reflection, dynamically calculating method names to call, and so on. Another criterion they used, was that the Ruby library should have an API compatible replacement written as a YARV C extension, thus indicating that the community (or at least one person in it) deemed the pure Ruby version too slow.

What they then did, was create some benchmarks that heavily rely on those two APIs and run them with the C extensions on YARV and the pure Ruby versions on TruffleRuby. The result was that TruffleRuby could execute the benchmarks on average at 0.8x the performance of YARV with the C extensions, and at best up to 21x that of YARV, in other words, TruffleRuby was able to optimize the Ruby code to a point where it was on average comparable to C, and in the best case, over 20x faster than C.

[I am simplifying here, you can read the whole story in a blog post by the lead developer: *Pushing Pixels with JRuby+Truffle].

That does, however, not mean that we can simply say "Ruby is 20x faster than C". It does, however, show that clever implementations for languages like Ruby (and Python, PHP, ECMAScript, etc. are not much different in that regard) can achieve comparable, and sometimes even better, performance than C.

There are more examples that demonstrate how throwing money at the problem increases performance. E.g. until companies like Google started to develop entire complex applications in ECMAScript (GMail, Google Docs, Google Wave [RIP], MS Office online, etc.), nobody really cared about ECMAScript performance. Sure, there were browser benchmarks, and browser vendors tried to improve them bit by bit, but there was no serious effort to build a fundamentally high-performance ECMAScript engine. Until Google built V8. Suddenly, all other vendors also invested heavily in performance, and within just a few years, ECMAScript performance had increased by a factor of 10 across all implementations. But the language had not changed at all in that time! So, the exact same language suddenly became "10 times faster", just by throwing money at it.

This should show that performance is not an inherent characteristic of the language.

One last example is Java. The original JVM by Sun was dog-slow. Along came a couple of Smalltalk guys who had developed a high-performance Smalltalk VM (the Animorphic Smalltalk VM) and noticed that Smalltalk and Java were very similar, and they could easily build a high-performance JVM using the same ideas. Sun bought the company (which is ironic, because the same developers had already built the high-performance Self VM based on the same ideas while employed at Sun, but Sun let them go just a couple of years earlier because they wanted to focus on Java and not Self as their new language), and the Animorphic Smalltalk VM became the Sun HotSpot JVM, still the most widely-used JVM to date.

(Interestingly, the team that built V8 includes key people of the team that built HotSpot, and the ideas behind V8 are – not surprisingly – also based on the Animorphic Smalltalk VM.)

Lastly, I would also like to point out that we have only talked about languages and language implementations (interpreters, compilers, VMs, …) here. But there is a whole environment around those. For example, modern CPUs contain quite a lot of features that are specifically designed to make C-like languages fast, e.g. branch prediction, memory prefetching, or memory protection. None of these features really help languages like Java, ECMAScript, PHP, Python, or Ruby. Some (e.g. memory protection) even have the potential to slow them down. (Virtual memory can impact garbage collection performance, for example.) The thing is: these languages are memory-safe and pointer-safe, they don't need memory protection because they fundamentally do not allow the operations that memory protection protects agains in the first place!

On a CPU and an OS that were designed for such languages, it would be much easier to achieve higher performance. If you really wanted to do a fair benchmark between, say, C and Python, you would have to run the Python code on a CPU that has received just as many optimizations for Python as our current mainstream CPUs have for C.

You might find some more interesting information in these questions:

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    $\begingroup$ Apart from the argument about throwing more money on an implementation, one may also consider what the same amount of money is spent on. Higher-level languages might want to focus on implementing the high-level features at all, and all the documentation, debuggability, and tooling surrounding this, rather than squeezing the last bit of performance out of micro-optimisations. It's developer efficiency that matter more for the more powerful languages. $\endgroup$ – Bergi Jun 22 at 16:31
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    $\begingroup$ Your TruffleRuby example is close to making the same mistake you caution against: you say "... can achieve comparable, and sometimes even better, performance than C." But it's not C in general you're comparing against. It's actually one specific implementation of some functions, compiled by a specific compiler for a given ISA, running on some specific hardware. You haven't ruled out the possibility of big optimizations to the version that uses some C. Perhaps it's reasonable to take it as "the best C can do on that platform", perhaps not. $\endgroup$ – Peter Cordes Jun 22 at 19:36
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    $\begingroup$ CPUs that execute high-level bytecode directly or have features dedicated to supporting it have been tried and rejected (e.g. ARM Jazelle, or Lisp machines) in favour of traditional CPUs that execute normal machine code, leaving it to software to JIT-compile. $\endgroup$ – Peter Cordes Jun 22 at 19:42
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    $\begingroup$ An additional example wrt Java might be certain concurrent problems that are actually vastly faster (and easier to implement) in Java than C++ because you can take advantage of the GC. Cliff Click from Azul systems talked about that in some ancient blog post - I think it was about his concurrent hashmap and the advantages of not having to worry about memory leaks and thereby avoiding fences. $\endgroup$ – Voo Jun 23 at 18:09
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    $\begingroup$ @Luaan: The real advantage of GC for concurrent problems is that GC solves the deallocation problem. In C++, the consumer side of a queue has to do the freeing (or queue the nodes again for later free). It thus has to make sure that no other thread could still have a pointer to the node before handing that memory back to the OS or using it for something else. This is hard, unless you already have a GC mechanism to lean on. $\endgroup$ – Peter Cordes Jun 24 at 7:09

In general, it's about what the language and its implementors are trying to do.

C has a long culture of keeping things as close to the hardware as possible. It doesn't do anything that could easily be translated into machine code at compile time. It was intended as a multi-platform kind of low level language. As time went on (and it was a lot of time!), C became sort of a target language for compilers in turn - it was a relatively simple way to get your language to compile for all the platforms that C compiled for, which was a lot of languages. And C ended up being the API-system of choice for most desktop software - not because of any inherent qualities in the way C calls things or shares header files or whatever, but simply because the barrier to introducing a new way is very high. So again, the alternatives usually sacrifice performance for other benefits - just compare C-style APIs with COM.

That isn't to say that C wasn't used for development, of course. But it's also clear that people were well aware of its shortcomings, since even people doing "hard-core" stuff like OS development always tried to find better languages to work with - LISP, Pascal, Objective-C etc. But C (and later C++) remained at the heart of most system-level stuff, and the compilers were continuously tweaked to squeeze out extra performance (don't forget there's ~50 years of C by now). C wasn't significantly improved in capabilities over that time; that was never seen as particularly important, and would conflict with the other design pillars.

Why do you design a new language? To make something better. But you can't expect to get everything better; you need to focus. Are you looking for a good way to develop GUIs? Build templates for a web server? Resolve issues with reliability or concurrency? Make it easier to write correct programs? Now, out of some of those, you may get performance benefits. Abstraction usually has costs, but it can also mean you can spend more of your time performance tweaking small portions of code.

It's definitely not true that using a low-level language (like C) will net you better performance. What is true, is that if you really really want to, you can reach the highest performance with a low-level language. As long as you don't care about the cost, maintainability and all that. Which is where economies of scale come in - if you can have a 100 programmers save performance for 100M programmers through a low-level tweak, that might be a great pay off. The same way, a lot of smart people working on a good high-level language can greatly increase the output of a lot more people using that language.

There is a saying that a sufficiently powerful compiler will be able to eliminate all the costs of high-level languages. In some sense, it's true - every problem eventually needs to be translated to a language the CPU understands, after all. Higher level abstractions mean you have fewer constraints to satisfy; a custom .NET runtime, for example, doesn't have to use a garbage collector. But of course, we do not have unlimited capacity to work on such compilers. So as with any optimisation problem, you solve the issues that are the most painful to you, and bring you the most benefit. And you probably didn't start the development of a new, high level language, to try to rival C in "raw" power. You wanted to solve a more specific problem. For example, it's really hard to write high-performance concurrent code in C. Not impossible, of course. But the "everything is shared and mutable by default" model means you have to either be extremely careful, or use plenty of guards everywhere. In higher level languages, the compiler or runtime can do that for you, and decide where those can be omitted.

More powerful programming languages tend to have slower implementations because fast implementations were never a priority, and may not be cost effective. Some of the higher level features or guarantees may be hard to optimise for performance. Most people don't think performance should trump everything - even the C and C++ people are using C or C++, after all. Languages often trade run-time, compile-time and write-time performance. And you don't even have to look at languages and their implementations to see that - for example, compare the original Doom engine with Duke Nukem 3D. Doom's levels need significant compile-time - Duke's can be edited in real-time. Doom had better runtime performance, but it didn't matter by the time Duke launched - it was fast enough, and that's all that matters when you're dealing with performance on a desktop.

What about performance on a server? You might expect a much stronger focus on performance in server software. And indeed, for things like database engines, that's true. But at the same time, servers are flooded with software like PHP or Node.js. Much of what's happening in server-space shifted from "squeeze every ounce of performance from this central server node" to "just throw a hundred servers at the problem". Web servers were always designed for high concurrency (and decentralisation) - that's one big reason why HTTP and the web were designed to be state-less. Of course, not everyone got the memo, and it's handy to have some state - but it still makes decoupling state from a particular server much easier. PHP is not a powerful language. It's not particularly nice to work with. But it provided something people needed - simple templating for their web sites. It took quite a while for performance to become an important goal, and it was further "delayed" by sharding, caching, proxying etc. - which were very simple to do thanks to the limitations of PHP and HTTP.

But surely, you'll always write an OS in C/C++? Well, for the foreseeable future on the desktop, sure. But not because of raw performance - the trump card is compatibility. Many research OSes have cropped up over time that provide greater safety, security, reliability and performance (particularly in highly concurrent scenarios). A fully memory managed OS makes many of the costs of managed memory go away; better memory guarantees, type safety and runtime type information allow you to elude many runtime checks and costs with task switching etc. Immutability allows processes to share memory safely and easily, at very low cost (heck, many of Unix strengths and weaknesses come from how fork works). Doing compilation on the target computer means you can't spend so much time optimising, but it also means you are targeting a very specific configuration - so you can always use the best available CPU extensions, for example, without having to do any runtime checks. And of course, safe dynamic code can bring its own performance benefits too (my software 3D renderer in C# uses that heavily for shader code; funnily enough, thanks to all the high-level language features, it's much simpler, faster and more powerful than e.g. the Build engine that powers Duke Nukem 3D - at the cost of extra memory etc.).

We're doing engineering here (poor as it may be). There's trade-offs to be had. Unless squeezing every tiny bit of performance out of your language gives you the greatest possible benefit, you shouldn't be doing it. C wasn't getting faster to please C programmers; it was getting faster because there were people who used it to work on stuff that made things faster for everyone else. That's a lot of history that can be hard to beat, and would you really want to spend the next 50 years catching up with some low-level performance tweaks and fixing tiny incompatibilities when nobody would want to use your language in the first place because it doesn't provide them with any real benefit over C? :)

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    $\begingroup$ "C has a long culture of keeping things as close to the hardware as possible. It doesn't do anything that could easily be translated into machine code at compile time. It was intended as a multi-platform kind of low level language." – I think that relationship has been reversed recently. Now, it's the CPU vendors who work hard to present emulation layers to make their CPUs look like a C Abstract Machine, precisely because C is not "portable assembly" or "close to the machine", but "close to the machine, as long as that machine is the PDP-11". $\endgroup$ – Jörg W Mittag Jun 25 at 11:21

I reject the premise.

  • A nuclear warhead is very powerful, but not very precise.
  • An acupuncture needle is very precise, and can be very powerful, but it is only leveraging the underlying neural system.
  • Lisp is very powerful, very precise, and yet, (some) people find it an awkward language.
  • APL is very very powerful, precise, and succinct. But it requires a special keyboard (or mapping), and is sometimes labelled as too difficult to teach (though it's probably fairer to say it's not for everyone).
  • Pascal isn't very powerful, but is fairly precise. It was designed as a teaching language, and also an experiment to prove that a one-pass compiler is practical. (Leave it to Microsoft to distribute a 3-pass compiler for a 1-pass language.)
  • Python, Perl, Java, etc. These are easier to write in for most people, there are loads of libraries, tutorials, and online projects for examination. Many of these languages don't have "pointers" as such, but do have "references", which are more consistent with the language -- you don't have to bother with pointer arithmetic, wrap-around, and other implementation-specific details. Indeed, these were meant to be on most, if not all, hardware. They are an abstraction up from C and C compilers, making their programs more widely applicable without recompiling. But they lose some performance for this flexibility.
  • Turing machines: the most powerful, and yet, when was the last time you wrote a program? Performance is awful, because, in all but pathological cases, there are better implementations.
  • GOL (Game Of Life): since it's Turing complete, it's just as powerful, yet the performance is worse than a direct Turing machine implementation in the same context.
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