This is responding to this paragraph of a post by Ola Bini, "Can Bytecodes Perform Well?" :
Let's see, what's next? Oh yeah. Bytecodes are always slow. No argument here. C++ will always beat a bytecode based engine. And C will almost always beat C++. And assembler (in the right hands) will usually beat C. But wait... Isn't Java bytecode based? And doesn't Java 6 perform on the same level as C in many cases, and in some cases performing better than C? Well, yes, it does... And wasn't Smalltalk always based on bytecodes? Most Smalltalk engines performed very well. Why is MRI switching to bytecodes for 1.9? And why has Python always been bytecode based? And why is the CLR bytecode based? Why was even Pascal using bytecodes back in the day? (OK, that is cheating... Pascal used bytecodes for portability, not performance, but it still worked well in that aspect too). Erlang is bytecode based.
He's taking issue with a comment of mine on his previous post; I quoted him saying, "[Rubinius] is byte code based. This means it's easier to handle performance," and flippantly responded, "If by 'handle' you mean 'not have'." 
I didn't say bytecodes are always slow. Bini's fast examples are actually native-code compilers, not bytecode interpreters. Bini's examples that aren't native-code compilers are slow. Python, for example, is slow when the inner loop is in Python bytecode. Erlang used to be really slow when it was a bytecode interpreter. OCaml's bytecode interpreter is considerably slower than its compiled code. But the OCaml native-code compiler is considerably simpler than the interpreter. On-the-fly "JIT" compilers don't need bytecode. In fact, bytecode may be a bad input format for JIT compilers. There exists an ahead-of-time Java compiler that doesn't use bytecode, and it produces code comparable to that produced by JIT compilers.
There are valid reasons to use bytecode, but performance is not one of them.
Several of Ola Bini's statements are dubious.
Detailed explanations of each of these statements follow.
My original statement was not that "bytecodes are always slow," but I can see how it would be interpreted that way. I said that building a language interpreter around bytecode makes it easier to not have performance. More specifically, I'm saying that building an interpreter as a bytecode interpreter does not make it fast, not even necessarily faster than today's popular Ruby interpreter.
Building an interpreter as an on-the-fly compiler does generally make it faster than a bytecode interpreter; recent Java virtual machines, the CLR, and the fast Smalltalk engines he's talking about are all on-the-fly native-code compilers, not bytecode interpreters. The fact that they take bytecodes rather than source code as input is incidental.
However, several of them are still fairly slow in absolute terms, even though they're much faster than interpreters.
At the time that Urs Hölzle wrote his dissertation , the ParcPlace Smalltalk system (release 4.0) ran about ten times faster than a deliberately naïve native-code compiler for Self (see section 4.4 of the dissertation), but it ran still around 4-10 times slower than C++ (see section 7.3.1). I don't know how fast Smalltalk systems are now, but I have the impression that they haven't sped up much, except for Strongtalk.
The Erlang HiPE just-in-time native-code compiler gets speedups of about 4 over the BEAM bytecode interpreter, but it's still about ten times slower than C in the "shootout" microbenchmarks. (See the section below about Erlang.)
Python, the bytecode Pascal compiler/interpreters, the BEAM bytecode Erlang interpreter, and bytecode implementations of Smalltalk (such as Squeak) are and were painfully slow for anything CPU-intensive. I used the UCSD P-System on a 4MHz Z80, and I use Python and Squeak today, and they are all painfully slow when you're doing anything CPU-bound --- unless you can push the inner loops out of the bytecode interpreter, as with NumPy. Typically the performance penalty over machine code is around a factor of 100; register-based "bytecode" interpreters like Wheat's, and Lua's often get the penalty down to a factor of 10 to 30.
You can see this pattern in the Great Programming Language Shootout results; the default weightings mostly represent CPU-intensive tasks.
If you look at the Python results, the tests where it was less than ten times slower than the C version are, with two exceptions, exactly the ones where the inner loop of the test was performed in C.
chameneos: tests thread rendezvous speed;
cheap-concurrency: tests coroutine creation speed; Python is faster than C here;
k-nucleotide: largely tests string hashing speed (note Python is 9x slower than C in this test);
nsieve: the "inner loop" here is essentially a NumPy-like array operation:
a[i + i::i] = (0,) * (m // i - 2 + (not not m % i))
regex-dna: the inner loop is in the sre regex engine, which is in C
pidigits: the inner loop is in gmpy, the Python binding to the GNU Multiple Precision library, which is in C
reverse-complement: the inner loops are in string.translate and reversing a list, which are done in C
partial-sums: this appears to be an exception; a straightforwardly written iterative Python solution is only six times slower than the similarly straightforwardly iterative C solution, doing a bunch of floating-point.
sum-file: the entire program contains no iteration in Python bytecode; the inner and only loop consists of four primitives implemented in C: sum, itertools.imap, int, and iter(sys.stdin).
Python uses "generator" objects for the cheap-concurrency test; these are implemented in the CPython interpreter as normal function activation records [[which presumably have some name]] rather than full threads. But the C version of the test uses full POSIX threads. A C version that used user-level "green threads" or "fibers" would probably do a little better, but it would still have to allocate a whole stack for each thread, rather than running them all on the same stack. The nearest C equivalent is probably Adam Dunkels's "protothreads".
I hypothesize that the "inner loop" in the chameneos case is actually in the underlying POSIX thread library, since the Python version is only 50% slower than the C version.
I don't know why the partial-sums test is so close.
On the tests where Python does worst, the Python program is 100-250 times slower than the corresponding C program; these are generally the tests where the Python version is most similar in structure to the C version. See, for instance, "recursive" (279x), "n-body" (163x), or "mandelbrot" (117x).
http://shootout.alioth.debian.org/gp4/benchmark.php?test=recursive&lang=all http://shootout.alioth.debian.org/gp4/benchmark.php?test=nbody&lang=all http://shootout.alioth.debian.org/gp4/benchmark.php?test=mandelbrot&lang=python&id=3
The large amount of variability between Python's best and worst results is mostly accounted for by variation in how much of the benchmark's inner loop is inside of some extension module or primitive implemented in machine code, and how much is actually executed by the bytecode interpreter.
Useful resources for comparing language implementation performances in this manner include Doug Bagley's original Great Programming Language Shootout from 2001 , the shootout.alioth.debian.org "Computer Language Benchmarks Game" version thereof, the Win32 version thereof at http://dada.perl.it/shootout/ , and Kernighan's "Timing Trials" paper .
Erlang today can use a native-code compiler called HiPE. The first versions of HiPE compiled bytecode to native code; current versions compile Erlang source to native code. HiPE sped up various Erlang microbenchmarks by factors of up to about 4 over the BEAM bytecode interpreter.  (Of course, some microbenchmarks hardly sped up at all.)
Many people think Erlang is still fairly slow; in the shootout microbenchmarks, even with HiPE, it tends to come in about 10x slower than C.
Although I previously saw speed ratios of only about a factor of 4 in some code, eyeballing numbers from the Win32 Shootout suggests that ocamlopt-generated native code is more typically 20-100 times faster than bytecode run by its bytecode interpreter.
This is kind of an interesting case, because both the native-code and bytecoded implementations of OCaml were implemented by a small team and are fairly compact. In Bini's post, he asserts, "Granted, a naive implementation of a bytecode engine will not perform well. But that is true for a compiler too."
David A. Wheeler's SLOCCount 2.26 tells me that the "bytecomp" and "byterun" subdirectories of OCaml-3.09.2 contain 12683 lines of C (in "byterun") and 8816 lines of ML (in "bytecomp"); I believe these 21.5KLOC represent the whole bytecode interpreter. (The parser, type-checker, standard library, etc., is shared with the native-code compiler.)
SLOCCount also tells me that the "asmcomp" and "asmrun" subdirectories contain 9457 lines of ML, 3910 lines of assembly, and 1077 lines of C, for a total of about 14.5KLOC. This includes backends for i386, ARM, AMD64, Itanium, PowerPC/POWER, SPARC, MIPS, HP-PA, and Alpha; the backends other than the i386 backend total about 2200 lines of ML and 3400 lines of assembly, so you could reduce that to about 10KLOC for a single CPU architecture.
In short, the native-code compiler for OCaml is about half the size of its interpreter, and so is presumably much more "naive", in the sense that it required much less effort to implement. (SLOCCount estimates three person-years, compared to five for the interpreter.) But it still performs one to two orders of magnitude better than the bytecode interpreter.
Although Bini anticipates this criticism by referring to OCaml's "extremely stringent type requirements" and calls it a "bondage-tightly typed language", this point generalizes beyond OCaml.
It's true that OCaml's semantics require much less type-checking and dynamic dispatching than Java, Python, or Ruby, and the overhead of type-checking in these more-dynamically-typed languages can be substantial compared to the overhead of dispatching bytecodes, which makes the savings of not dispatching bytecodes less noticeable. But the next few sections show that the performance advantages of native-code compilation are not limited to static languages like OCaml; indeed, the performance benefits Bini attributes to the use of bytecode are actually due to the use of native-code compilation.
In Bini's post, he asserts:
...Java and the CLR family of languages use bytecodes because it gives the runtime system the opportunity to dynamically change the machine code running based on statistics and criteria found out during runtime. ... This is not possible in a clean compilation to machine code. Java would never have had the performance it has now if it weren't for the bytecodes.
This is simply mistaken. There are on-the-fly machine-code compilers that work from source code instead of bytecode. SBCL and the current HiPE compiler are examples. There are some advantages to compiling directly to machine code from source code rather than compiling from a bytecode format intended for interpretation; typically the translation to bytecode erases a lot of information that is helpful to optimization.
As one of the HiPE papers  explains:
A new feature, described further below, is that the HiPE compiler can compile directly from Core Erlang [a restricted subset of Erlang]. When used in this way, the compiler compiles a whole module at a time, and performs global analyses and optimizations which are significantly more difficult to perform (and thus not available) in the traditional mode [which compiled from the BEAM bytecodes].
The GCC Java compiler, GCJ, can compile either Java source of Java bytecode into machine code for many different processors. In the past, it could do some optimizations when compiling from Java source that it couldn't do when compiling from Java bytecode . However, apparently, now it can do essentially the same set of optimizations for Java bytecode   --- it just took more work --- and GCJ is switching to the Eclipse Java frontend for parsing Java, starting with GCC 4.3 . In this case, the standard bytecode permits successful independent development of successive links in the toolchain.
The above notes are about traditional all-at-once or "ahead-of-time" compilers, rather than the piecemeal profile-directed-optimizing compilation used by good Smalltalk implementations, Self, and HotSpot. There are some differences: JIT compilers generally have to run faster than ahead-of-time compilers. I don't believe this negates my point; although parsing code takes some time, the compiler can maintain a "bytecode" parsed intermediate representation of the program without supporting it as an input format, and you can just as well cache an AST. (Also, bytecode-like linear quadruple and stack-machine intermediate representations in compilers are losing mindshare these days to other alternatives like CPS and GCC's tree-SSA.)
In general, optimizing an intermediate representation (such as a bytecode format) for high-speed interpretation tends to make it worse for dynamic machine-code generation, and vice versa. Good formats for dynamic machine-code generation tend to contain most or all of the information in the original source code; good formats for rapid interpretation erase as much of that information as possible.
Self pioneered the techniques of profile-based optimization, specialization, and type feedback that Bini refers to above, and which make it possible for current Java JIT compilers to do a reasonably good job, despite the dynamic nature of the Java language. Self's bytecode format was little more than an RPN tokenized version of the source code, with blocks separated into their own bytecode objects; quite different from the Smalltalk-80 bytecode format, in which control structures are generally already inlined, and with special bytecodes for instance variable access, local variable access, and common method names.
More recent efficient bytecode-VM designs like Lua's are register-based rather than stack-based, which again, makes interpretation faster, but dynamic compilation more difficult.
I don't know for sure why Microsoft's architects chose to make the CLR bytecode-based, but my guess is that it's a weak method of source-code obfuscation. Some of their customers would have balked at shipping source code to their "assemblies", the way they have to do if they're written in Perl or Erlang, and so they would have continued using Java or shipping blobs of x86 machine code instead.
Bini's statement, "Java would never have had the performance it has now if it weren't for the bytecodes," is mistaken for another reason, other than merely that other programming languages. As mentioned above, GCJ can compile Java from source code to machine code without an intermediate bytecode step; so clearly the use of bytecode is not crucial to whatever performance GCJ-compiled code achieves. So what does it achieve?
In many cases, despite not using specialization or profile-directed feedback (which are easier to do in JIT compilers than in ahead-of-time compilers like GCJ), GCJ-compiled programs run around the same speed as JIT-compiled programs. The only systematic set of benchmarks I've found, from 2004, shows GCJ-compiled programs running a little less than half as fast as the same program in the 1.4 or 1.5 IBM or Sun JDK. 
There is also a variety of anecdotal evidence from real applications; some of it shows programs running a little faster with GCJ, and some of it shows them running a little slower. I haven't been able to find anything comparing the performance of GCJ 4.x, which was supposed to have a lot of new optimizations, to anything else, or a comparison of Java 6 to any version of GCJ.
As far as I know, GCJ does not yet take advantage of type feedback from the compiled program, which could put it at a substantial performance disadvantage to a JIT compiler.
"Performance measurements: Java and C++", by Jean-Marc Vanel, 2003
"Compiling Java with GCJ", by Per Bothner, 2003-01-01, published in Linux Journal, says, "Truthfully, running a program compiled by GCJ is not always noticeably faster than running it on a JIT-based Java implementation; sometimes it even may be slower ... GCJ is often significantly faster than alternative JVMs, and it is getting faster as people improve it. ... Running the Kawa test suite using GCJ vs. JDK1.3.1, GCJ is about twice as fast..."
"comparison between native gcj and bytecode", mailing list post by Erik Poupaert, 2003-01-05, to Prof. Laurie Hendren, posted to sablecc-list, saying, "There are reasons to believe that gcj already beats the JDK with regards to performance."
"Performance comparison", mailing list post by Norman Hendrich, posted to email@example.com on 2002-07-29, showing his program running with GCJ at 72% of its JDK 1.3.1 speed and 47% of its JDK 1.4.0 speed
"Linux Number Crunching: Benchmarking Compilers and Languages for ia32", by Scott Robert Ladd, 2003-01-04, in which he benchmarked some computational astronomy code to measure floating-point performance; GCJ's best speeds were within 10% of the best 1.3 and 1.4 JIT JVM speeds (faster than the JIT VMs on one machine, slower on the other), but roughly three times slower than the Intel C++ and Fortran compilers.
http://web.archive.org/web/20040803034751/http://www.coyotegulch.com/reviews/almabench.html An incomplete updated version (missing the results!) is online at http://www.coyotegulch.com/reviews/number_crunching/
Bytecode can be considerably more compact than machine code, source code, or even gzipped source code, as a distribution format, and it uses dramatically less memory than abstract syntax trees, machine code, or source code. Bytecode interpreters are considerably more portable than native-code compiler backends. Finally, it's a lot easier to hack single-stepping and other debugging facilities into a bytecode interpreter than to implement them for even one CPU/OS combination, let alone the whole range of CPU/OS combinations a particular language implementation might need to support.
These can all be valid reasons for choosing a bytecode-interpreter architecture for your language implementation rather than a native-code-compiler architecture, although they're not nearly as strong now as they were in the past (say, when Pascal was being developed, and there were literally dozens of incompatible CPU architectures in common use, some with several operating systems.) But performance is not a valid reason for this.
I wrote a kragen-tol post with more detail about this in March. 
Someone might argue that a machine-code compiler is inherently more complex than a bytecode interpreter, but I don't think that's necessarily true. The OCaml interpreter/compiler comparison above is one data point. As another, in 2003, I hacked together a machine-code "compiler" from parse trees for arithmetic expressions, using gcc to actually generate the machine code, in just a few hours . But I don't have enough experience building machine-code-generating backends to say for sure.
Bini ends his post, "So please, stop spreading this myth. It is NOT true and it has NEVER been true."
I think the myth he refers to is that bytecode interpreters are slow. But as shown both by his post and this note, pure bytecode interpreters are indeed considerably slower than native machine code. While native-code compilers that compile from bytecode can produce fast code, that's not because they use bytecode as an input format; that's because they're native-code compilers, often highly-tuned native-code compilers that use run-time profiling and type feedback information, and native code can run pretty fast. Even naively-generated native code rarely runs as slow as bytecode in a bytecode interpreter. (See section 4.5 of Urs Hölzle's dissertation ; the "non-inlining compiler" of Self-93 was 2600 lines of C++, one-fifth the size of the OCaml bytecode interpreter, and Hölzle argues that bytecode interpretation would be hard-pressed to do better.)
But Bini originally said, "[Rubinius] is byte code based. This means it's easier to handle performance." It's a pretty ambiguous statement (easier than what? what does "handle" mean?) but I think this note has adequately outlined the degree to which the obvious interpretations are false. While there are slower language implementation techniques than bytecode interpreters available, such as those used by bash or Tcl 7, they aren't in wide use.
Rubinius may well achieve excellent performance, or it may not, but its use of bytecode is not particularly relevant to that goal.
 "Can Bytecodes Perform Well?", by Ola Bini, 2007-09-24, on his blog
 flippant comment on Ola Bini's blog post "Rubinius is Important", by Kragen Javier Sitaker
 "OCaml vs. SBCL, and various other interpreters", Kragen Sitaker, posted to the kragen-tol mailing list on 2007-03-12
 "compiling Python arithmetic expressions to machine code", Kragen Sitaker, posted to the kragen-hacks mailing list on 2003-02-14
 "All you wanted to know about the HiPE compiler (but might have been afraid to ask)", by K. Sagonas, M. Pettersson, R. Carlsson, P. Gustafsson, and T. Lindahl, July 2003, 7 pp.
http://user.it.uu.se/~kostis/Papers/erlang03.pdf or http://www.erlang.se/workshop/2003/paper/p36-sagonas.pdf linked from http://www.erlang.se/publications/publications.shtml
 Mailing list post "Reconsidering gcjx", from Tom Tromey to the firstname.lastname@example.org and email@example.com mailing lists, posted 2006-01-26; in particular, see the part "Technical approach", which lists three optimizations previously available only with the .java front end.
 Question 4.2 in the GCJ FAQ, "Can GCJ only handle source code?", most of which answer is written by Per Bothner; the page says it was last updated 2007-08-19.
 Mailing list post on the thread "Reconsidering gcjx", from Tom Tromey to the firstname.lastname@example.org and email@example.com mailing lists, posted 2006-01-29, in which he talks about losing "a small optimization related to String "+" operations".
 GCJ news item from 2007-01-08: "We've merged the gcj-eclipse branch to svn trunk... This new code will appear in GCC 4.3." Currently this is on the GCJ home page:
http://www.gnu.org/software/gcc/java/index.html But eventually it will probably move to the "Less Recent GCJ news" page: http://www.gnu.org/software/gcc/java/news.html
 "Timing Trials, or, the Trials of Timing: Experiments with Scripting and User-Interface Languages", by Brian W. Kernighan and Christopher J. Van Wyk, 1998
 Doug Bagley's Great Programming Language Shootout is now no longer available from Doug's site, but it can be found on the Internet Archive:
 "Performance Comparison of Java/.NET Runtimes (Oct 2004)", by Kazuyuki Shudo, 2005-11-20
 "Adaptive Optimization for Self: Reconciling High Performance with Exploratory Programming", by Urs Hölzle, August 1994, his doctoral dissertation.
[[??? XXX]] Linked from the Sun Self Research Papers page: http://research.sun.com/self/papers/papers.html
 [[Chambers ]]
Notes from Hölzle's dissertation:
Thus, we believe that type feedback is probably easier to add to a conventional batch-style compilation system. ... As mentioned above, static compilation has the advantage that the compiler has complete information since optimization starts after a complete program execution. ... On the other hand, a dynamic recompilation system has a significant advantage because it can dynamically adapt to changes in the program's behavior.
(section 5.6, p. 42, "Adding type feedback to a conventional system")
The combination of SOAR's software and hardware features was very successful when compared with other Smalltalk implementations on CISC machines: with a 400 ns cycle time, SOAR ran as fast as the 70 ns microcoded Xerox Dorado workstation and about 25% faster than the Deutsch-Schiffman Smalltalk system running on a Sun-3 with a cycle time of about 200 ns. However, as we will see in Chapter 8, the optimization techniques used by the SELF compiler greatly reduces the performance benefit of special hardware support.
(section 220.127.116.11, p. 11; SOAR is a non-dynamic native-code compiler running on a customized version of the Berkeley RISC II processor)
Chambers's dissertation mentions some stuff about Dorado comparative performance:
The definition of Smalltalk-80 specifies that source code methods are translated into byte codes, the machine instructions of a stack machine. Originally, Smalltalk-80 ran on Xerox Dorados implementing this instruction set in microcode [Deu83]. Subsequent software implementations of Smalltalk-80 on stock hardware supplied a virtual machine that interpreted these byte codes in software. Needless to say this interpretation was quite slow [Kra83].
[Kra83] Glenn Krasner, editor. Smalltalk-80: Bits of History, Words of Advice. Addison-Wesley, Reading, MA, 1983.