The three most influential factors for Eclipse speed are:
- Using the latest version of Eclipse (2020-06 as on 26 June 2020)
Note that David Balažic's comment (July 2014) contradicts that criteria which was working six years ago:
The "same" workspace in Indigo (3.7.2) SR2 loads in 4 seconds, in Kepler SR2 (4.3.2) in 7 seconds and in Luna (4.4.0) in 10 seconds. All are Java EE bundles. Newer versions have more bundled plugins, but still the trend is obvious. (by "same" workspace I mean: same (additionally installed) plugins used, same projects checked out from version control).
Launching it with the latest JDK (Java 14 at the time of writing, which does not prevent you to compile in your Eclipse project with any other JDK you want: 1.4.2, 1.5, 1.6 older...)
-vm jdk1.6.0_10\jre\bin\client\jvm.dll
Configuring the eclipse.ini (see this question for a complete eclipse.ini)
-Xms512m
-Xmx4096m
[...]
The Xmx argument is the amount of memory Eclipse will get (in simple terms). With -Xmx4g, it gets 4 GB of RAM, etc.
Note:
- Referring to the jvm.dll has advantages:
- Splash screen coming up sooner.
- Eclipse.exe in the process list instead of java.exe.
- Firewalls: Eclipse wants access to the Internet instead of Java.
- Window management branding issues, especially on Windows and Mac.
Dec. 2020, Udo conforms in the comments
From version 4.8 (Photon) an up there was a steady speed gain after each version.
The main platform was optimized every release to load faster, enable more features for the dark theme and to add more features for newer Java versions for the Java development tools.
Especially with-in the last 3 versions the startup time was increased a lot. There should be a significant increase in start-up time with the newest version of Eclipse 2020-12.
In my experience it started a lot faster with each new version.
But: There are still plug-ins which do not follow the new way of using the Eclipse API and are therefore still slow to start.
Since the change to Java 11 as the minimum runtime version starting from Eclipse version 2020-09 at least the core system uses the newer features of the JVM. It is up to the providers of the other plug-ins to upgrade to newer APIs and to use the full power of modern CPUs (e.g. concurrent programming model).
Here is a real world example: Fixed point multiplies on old compilers.
These don't only come handy on devices without floating point, they shine when it comes to precision as they give you 32 bits of precision with a predictable error (float only has 23 bit and it's harder to predict precision loss). i.e. uniform absolute precision over the entire range, instead of close-to-uniform relative precision (float).
Modern compilers optimize this fixed-point example nicely, so for more modern examples that still need compiler-specific code, see
C doesn't have a full-multiplication operator (2N-bit result from N-bit inputs). The usual way to express it in C is to cast the inputs to the wider type and hope the compiler recognizes that the upper bits of the inputs aren't interesting:
// on a 32-bit machine, int can hold 32-bit fixed-point integers.
int inline FixedPointMul (int a, int b)
{
long long a_long = a; // cast to 64 bit.
long long product = a_long * b; // perform multiplication
return (int) (product >> 16); // shift by the fixed point bias
}
The problem with this code is that we do something that can't be directly expressed in the C-language. We want to multiply two 32 bit numbers and get a 64 bit result of which we return the middle 32 bit. However, in C this multiply does not exist. All you can do is to promote the integers to 64 bit and do a 64*64 = 64 multiply.
x86 (and ARM, MIPS and others) can however do the multiply in a single instruction. Some compilers used to ignore this fact and generate code that calls a runtime library function to do the multiply. The shift by 16 is also often done by a library routine (also the x86 can do such shifts).
So we're left with one or two library calls just for a multiply. This has serious consequences. Not only is the shift slower, registers must be preserved across the function calls and it does not help inlining and code-unrolling either.
If you rewrite the same code in (inline) assembler you can gain a significant speed boost.
In addition to this: using ASM is not the best way to solve the problem. Most compilers allow you to use some assembler instructions in intrinsic form if you can't express them in C. The VS.NET2008 compiler for example exposes the 32*32=64 bit mul as __emul and the 64 bit shift as __ll_rshift.
Using intrinsics you can rewrite the function in a way that the C-compiler has a chance to understand what's going on. This allows the code to be inlined, register allocated, common subexpression elimination and constant propagation can be done as well. You'll get a huge performance improvement over the hand-written assembler code that way.
For reference: The end-result for the fixed-point mul for the VS.NET compiler is:
int inline FixedPointMul (int a, int b)
{
return (int) __ll_rshift(__emul(a,b),16);
}
The performance difference of fixed point divides is even bigger. I had improvements up to factor 10 for division heavy fixed point code by writing a couple of asm-lines.
Using Visual C++ 2013 gives the same assembly code for both ways.
gcc4.1 from 2007 also optimizes the pure C version nicely. (The Godbolt compiler explorer doesn't have any earlier versions of gcc installed, but presumably even older GCC versions could do this without intrinsics.)
See source + asm for x86 (32-bit) and ARM on the Godbolt compiler explorer. (Unfortunately it doesn't have any compilers old enough to produce bad code from the simple pure C version.)
Modern CPUs can do things C doesn't have operators for at all, like popcnt or bit-scan to find the first or last set bit. (POSIX has a ffs() function, but its semantics don't match x86 bsf / bsr. See https://en.wikipedia.org/wiki/Find_first_set).
Some compilers can sometimes recognize a loop that counts the number of set bits in an integer and compile it to a popcnt instruction (if enabled at compile time), but it's much more reliable to use __builtin_popcnt in GNU C, or on x86 if you're only targeting hardware with SSE4.2: _mm_popcnt_u32 from <immintrin.h>.
Or in C++, assign to a std::bitset<32> and use .count(). (This is a case where the language has found a way to portably expose an optimized implementation of popcount through the standard library, in a way that will always compile to something correct, and can take advantage of whatever the target supports.) See also https://en.wikipedia.org/wiki/Hamming_weight#Language_support.
Similarly, ntohl can compile to bswap (x86 32-bit byte swap for endian conversion) on some C implementations that have it.
Another major area for intrinsics or hand-written asm is manual vectorization with SIMD instructions. Compilers are not bad with simple loops like dst[i] += src[i] * 10.0;, but often do badly or don't auto-vectorize at all when things get more complicated. For example, you're unlikely to get anything like How to implement atoi using SIMD? generated automatically by the compiler from scalar code.
Best Answer
Good answers. I would also suggest writing a small compiler and having it write the assembly language for you. That way, you get to think about different ways to do things in assembler, like passing arguments, making stack frames, composing address expressions, array indexing, managing memory, conditionals, loops, try-catch, etc. etc.