Show HN: Threadprocs – executables sharing one address space (0-copy pointers)

Interesting.

I gotta admit that my smell test tells me that this is a step backwards; at least naively (I haven't looked through the code thoroughly yet), this just kind of feels like we're going back to pre-protected-memory operating systems like AmigaOS; there are reasons that we have the process boundary and the overhead associated with it.

If there are zero copies being shared across threadprocs, what's to stop Threadproc B from screwing up Threadproc A's memory? And if the answer to this is "nothing", then what does this buy over a vanilla pthread?

I'm not trying to come off as negative, I'm actually curious about the answer to this.

It is lovely to see experimentation like this going on. I think this has a lot of potential with something like Haskell's green threading model, basically taking it up a notch and doing threading across pthreads instead of being restricted to the VM threads. Surely, if this can be well fleshed-out, it could be implemented at the compiler-level or library-level so existing multi-threaded Haskell software can switch over to something like this to squeeze out even more performance. I'm not an expert here, though, so ¯\_(ツ)_/¯ take my words with a grain of salt.

I actually just published a paper about something like this, which I implemented in both libriscv and TinyKVM called "Inter-Process Remote Execution (IPRE): Low-latency IPC/RPC using merged address spaces".

Here is the abstract: This paper introduces Inter-Process Remote Execution (IPRE), whose primary function is enabling gated persistence for per-request isolation architectures with microsecond-latency access to persistent services. IPRE eliminates scheduler dependency for descheduled processes by allowing a virtual machine to directly and safely call, execute functions in a remote virtual machines address space. Unlike prior approaches requiring hardware modifications (dIPC) or kernel changes (XPC), IPRE works with standard virtualization primitives, making it immediately deployable on commodity systems. We present two implementations: libriscv (12-14ns overhead, emulated execution) and TinyKVM (2-4us overhead, native execution). Both eliminate data serialization through address-space merging. Under realistic scheduler contention from schbench workloads (50-100% CPU utilization), IPRE maintains stable tail latency (p99Bottom line: If you're doing synchronous calls to a remote party, IPRE wouldn't require any scheduler mediation. The same applies to your repo. Passing allocator-less structures to the remote is probably a landmine waiting to happen. If you structure both parties to use custom allocators, at least for the remote calls, you can track and even steal allocations (using a shared memory area). With IPRE there is extra risk of stale pointers because the remote part is removed from the callers memory after it completes. The paper will explain all the details, but for example since we control the VMM we can close the remote session if anything bad happens. (This paper is not out yet, but it should be very soon)

The best part about this kind of architecture, which you immediately mention, is the ability to completely avoid serialization. Passing a complex struct by reference and being able to use the data as-is is a big benefit. It breaks down when you try to do this with something like Deno, unfortunately. But you could do Deno C++, for example.

For libriscv the implementation is simpler: Just loan remote-looking pages temporarily so that read/write/execute works, and then let exception-handling handle abnormal disconnection. With libriscv it's also possible for the host to take over the guests global heap allocator, which makes it possible to free something that was remotely allocated. You can divide the address space into the number of possible callers, and one or more remotes, then if you give the remote a std::string larger than SSO, the address will reveal the source and the source tracks its own allocations, so we know if something didn't go right. Note that this is only an interest for me, as even though (for example) libriscv is used in large codebases, the remote RPC feature is not used at all, and hasn't been attemped. It's a Cool Idea that kinda works out, but not ready for something high stakes.

How would this really help python though? This doesn't solve the difficult problem, which is that python objects don't support parallel access by multiple threads/processes, no? Concurrent threads, yes, but only one thread can be operating on a python object at a time (I'm simplifying here for brevity).

There are already means of passing around bulk data with zero copy characteristics in python, but there's a lot of bureaucracy around it. A true solution must work with the GIL (or remove it altogether), no?

Judging by the description, it's exactly like AmigaOS, Windows 3.x or early Apple. Or MS-DOS things like DESQview.

I fail to see the point - if you control the code and need performance so much that an occassional copy bites, you can as well just link it all into a single address space without those hoopjumps. It won't function as separate processes if it's modified to rely on passing pointers around anyway.

And if you don't, good luck chasing memory corruption issues.

Besides, what's wrong with shared memory?