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You don’t see how a collector actually performs unless you run it on the kind of large, messy real-world programs it is actually intended for. Give it a try and see if you can fix any yourself. You can see where this is going. Clearly the baking analogy is going to my head. You are very likely to hit the first bug. These failures often don’t immediately cause a crash, which makes it hard for us to trace backward in time to find the bug. That doesn’t really do anything useful (aside from ensuring that they don’t incorrectly get deleted). The updatedb shellscript, after setting the locale to "C" & parsing commandline flags, determines whether sort supports "-z", loads & validates configuration from envvars, validates frcode is present, deletes the old tempfile & configures it’s replacement to be deleted upon exit signals, runs find twice as configured (both optional, both have 2 different codepaths) piped into sort & frcode with a failure handler, then mvs the temp file into place. Regardless of whether it does that bitmask analysis it checks spillage behaviour then iterates over the allocnos & their objects, bitwise-or’ing conflicting regs based on different conditions.

This pass optionally checks against passing NULL. In my GCC discussions, it looks like I’ve skipped over a pass which unrolls the outer loop around some innermost loop. As such GCC needs to optimize them away as much as possible, and for that it needs to gather some data. Followed by one outputting DWARF2 annotations for the sake of tools like GCC. A critical edge is one with more than 2 of both successors & predecessors in the dominators tree. If you’ve never written any machine code, or its slightly more human-palatable cousin assembly code before, I’ll give you the gentlest of introductions. After more debugging/profiling late IPA passes are run & figures out which functions we want to output. In theory, we are all done now. When the GC is marking roots, it walks the chain of compilers and marks each of their functions, so the new constant is reachable now. Next, we define a convenience method to add a new constant to the chunk.

That in turn triggers a GC, which fails to mark the new constant object and thus sweeps it right before we have a chance to add it to the table. We had to make sure the object was on the value stack so that the collector’s mark phase would find it and keep it alive. It adds value to chunk’s constant array and then writes an appropriate instruction to load the constant. The sweep phase frees objects by calling reallocate(), which lowers the value of bytesAllocated, so after the collection completes, we know how many live bytes remain. As far as I know, we’ve found all of the collection bugs in clox, and now we have a working, robust, self-tuning, mark-sweep garbage collector. It’s not like other bugs where you’re looking for the code that causes some problem. We could pawn the problem onto the user and force them to pick by exposing GC tuning parameters.

Time visiting live objects is time not freeing memory and also time not running user code. The first is a running total of the number of bytes of managed memory the VM has allocated. This way, as the amount of memory the program uses grows, the threshold moves farther out to limit the total time spent re-traversing the larger live set. This will obviously expand over time, so we’ll set up a new module to give ourselves room to grow. The VM will spend all of its time obsessively revisiting the same set of live objects over and over, and throughput will suffer. It is a slightly smaller alternative, which provides the same amount of joy. There’s 4 callback states for defining those entities, reusing much of the same code as the previous state. But how do we find the balance between these when we have no idea how much memory the user’s program needs and how often it allocates?

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