hard to believe that Java could possibly be as fast or faster than C++ .
hard to believe that Java could possibly be as fast or faster than C++
assertion has yet to be proven to my satisfaction. However, I’ve begun to
see that many of my doubts about
come from early implementations that were not particularly efficient so there
was no model at which to point to explain how Java could be fast.
of the way I’ve thought about speed has come from being cloistered with
model. C++ is very focused on everything happening statically, at compile time,
so that the run-time image of the program is small and fast. C++ is also based
directly on the C model, primarily for backwards compatibility, but sometimes
simply because it worked a particular way in C so it was the easiest approach
in C++. One of the most important cases is the way memory is managed in C and
C++, and this has to do with one of my more fundamental assertions about why
Java must be slow: in Java, all objects must be created on the heap.
C++, creating objects on the
is fast because when you enter a particular scope the stack pointer is moved
down once to allocate storage for all the stack-based objects created in that
scope, and when you leave the scope (after all the local destructors have been
called) the stack pointer is moved up once. However, creating heap objects in
C++ is typically much slower because it’s based on the C concept of a
heap as a big pool of memory that (and this is essential) must be recycled.
When you call
in C++ the released memory leaves a hole in the heap, so when you call
the storage allocation mechanism must go seeking to try to fit the storage for
your object into any existing holes in the heap or else you’ll rapidly
run out of heap storage. Searching for available pieces of memory is the reason
that allocating heap storage has such a performance impact in C++, so
it’s far faster to create stack-based objects.
because so much of C++ is based on doing everything at compile-time, this makes
sense. But in Java there are certain places where things happen more
dynamically and it changes the model. When it comes to creating objects, it
turns out that the garbage collector can have a significant impact on
increasing the speed of object creation. This might sound a bit odd at first
– that storage release affects storage allocation – but it’s
the way some JVMs work and it means that allocating storage for heap objects in
Java can be nearly as fast as creating storage on the stack in C++.
can think of the C++ heap (and a slow implementation of a Java heap) as a yard
where each object stakes out its own piece of turf. This real estate can become
abandoned sometime later and must be reused. In some JVMs, the Java heap is
quite different; it’s more like a conveyor belt that moves forward every
time you allocate a new object. This means that object storage allocation is
remarkably rapid. The “heap pointer” is simply moved forward into
virgin territory, so it’s effectively the same as C++’s stack
allocation. (Of course, there’s a little extra overhead for bookkeeping
but it’s nothing like searching for storage.)
you might observe that the heap isn’t in fact a conveyor belt, and if you
treat it that way you’ll eventually start paging memory a lot (which is a
big performance hit) and later run out. The trick is that the garbage collector
steps in and while it collects the garbage it compacts all the objects in the
heap so that you’ve effectively moved the “heap pointer”
closer to the beginning of the conveyor belt and further away from a page
fault. The garbage collector rearranges things and makes it possible for the
high-speed, infinite-free-heap model to be used while allocating storage.
understand how this works, you need to get a little better idea of the way the
different garbage collector (GC) schemes work. A simple but slow GC technique
is reference counting. This means that each object contains a reference
counter, and every time a handle is attached to an object the reference count
is increased. Every time a handle goes out of scope or is set to
the reference count is decreased. Thus, managing reference counts is a small
but constant overhead that happens throughout the lifetime of your program. The
garbage collector moves through the entire list of objects and when it finds
one with a reference count of zero it releases that storage. The one drawback
is that if objects circularly refer to each other they can have non-zero
reference counts while still being garbage. Locating such self-referential
groups requires significant extra work for the garbage collector. Reference
counting is commonly used to explain one kind of garbage collection but it
doesn’t seem to be used in any JVM implementations.
faster schemes, garbage collection is not based on reference counting. Instead,
it is based on the idea that any non-dead object must ultimately be traceable
back to a handle that lives either on the stack or in static storage. The chain
might go through several layers of objects. Thus, if you start in the stack and
the static storage area and walk through all the handles you’ll find all
the live objects. For each handle that you find, you must trace into the object
that it points to and then follow all the handles in
object, tracing into the objects they point to, etc., until you’ve moved
through the entire web that originated with the handle on the stack or in
static storage. Each object that you move through must still be alive. Note
that there is no problem with detached self-referential groups – these
are simply not found, and are therefore automatically garbage.
the approach described here, the JVM uses an
scheme, and what it does with the live objects that it locates depends on the
variant currently being used. One of these variants is
This means that, for reasons that will become apparent, the program is first
stopped (this is not a background collection scheme). Then, each live object
that is found is copied from one heap to another, leaving behind all the
garbage. In addition, as the objects are copied into the new heap they are
packed end-to-end, thus compacting the new heap (and allowing new storage to
simply be reeled off the end as previously described).
course, when an object is moved from one place to another, all handles that
point at (reference) that object must be changed. The handle that comes from
tracing to the object from the heap or the static storage area can be changed
right away, but there can be other handles pointing to this object that will be
encountered later during the “walk.” These are fixed up as they are
found (you could imagine a hash table mapping old addresses to new ones).
are two issues that make copy collectors inefficient. The first is the idea
that you have two heaps and you slosh all the memory back and forth between
these two separate heaps, maintaining twice as much memory as you actually
need. Some JVMs deal with this by allocating the heap in chunks as needed and
simply copying from one chunk to another.
second issue is the copying. Once your program becomes stable it might be
generating little or no garbage. Despite that, a copy collector will still copy
all the memory from one place to another, which is wasteful. To prevent this,
some JVMs detect that no new garbage is being generated and switch to a
different scheme (this is the “adaptive” part). This other scheme
and it’s what Sun’s JVM uses all the time. For general use mark and
sweep is fairly slow, but when you know you’re generating little or no
garbage it’s fast.
and sweep follows the same logic of starting from the stack and static storage
and tracing through all the handles to find live objects. However, each time it
finds a live object that object is marked by setting a flag in it, but the
object isn’t collected yet. Only when the marking process is finished
does the sweep occur. During the sweep, the dead objects are released. However,
no copying happens, so if the collector chooses to compact a fragmented heap it
does so by shuffling objects around.
“stop-and-copy” refers to the idea that this type of garbage
done in the background; instead, the program is stopped while the GC occurs. In
the Sun literature you’ll find many references to garbage collection as a
low-priority background process, but it turns out that this was a theoretical
experiment that didn’t work out. In practice, the Sun garbage collector
is run when memory gets low. In addition, mark-and-sweep requires that the
program be stopped.
previously mentioned, in the JVM described here memory is allocated in big
blocks. If you allocate a large object, it gets its own block. Strict
stop-and-copy requires copying every live object from the source heap to a new
heap before you could free the old one, which translates to lots of memory.
With blocks, the GC can typically use dead blocks to copy objects to as it
collects. Each block has a
to keep track of whether it’s alive. In the normal case, only the blocks
created since the last GC are compacted; all other blocks get their generation
count bumped if they have been referenced from somewhere. This handles the
normal case of lots of short-lived temporary objects. Periodically, a full
sweep is made – large objects are still not copied (just get their
generation count bumped) and blocks containing small objects are copied and
compacted. The JVM monitors the efficiency of GC and if it becomes a waste of
time because all objects are long-lived then it switches to mark-and-sweep.
Similarly, the JVM keeps track of how successful mark-and-sweep is, and if the
heap starts to become fragmented it switches back to stop-and-copy. This is
where the “adaptive” part comes in, so you end up with a mouthful:
“adaptive generational stop-and-copy mark-and-sweep.”
are a number of additional speedups possible in a JVM. An especially important
one involves the operation of the loader and Just-In-Time (JIT) compiler. When
a class must be loaded (typically, the first time you want to create an object
of that class), the
file is located and the byte codes for that class are brought into memory. At
this point, one approach is to simply JIT all the code, but this has two
drawbacks: it takes a little more time, which, compounded throughout the life
of the program, can add up; and it increases the size of the executable (byte
codes are significantly more compact than expanded JIT code) and this might
cause paging, which definitely slows down a program. An alternative approach is
which means that the code is not JIT compiled until necessary. Thus, code that
never gets executed might never get JIT compiled.
JVMs are external to browsers, you might expect that you could benefit from the
speedups of some JVMs while using any browser. Unfortunately, JVMs don’t
currently interoperate with different browsers. To get the benefits of a
particular JVM, you must either use the browser with that JVM built in or run
standalone Java applications.