Tootfinder

I’m rewriting my logic language code in Haskell because I got frustrated with Rust’s verbosity and having to deal with garbage collection. I didn’t feel like it was adding enough, and I started to rethink my reasoning for using Rust (so it can be embedded anywhere). I don’t know if this language is going anywhere, maybe it’s fine if I have to rewrite it once it’s popular. It may never be.

from my link log —
When should a Java JIT compiler expand garbage collection barriers?
https://robcasloz.github.io/blog/2024/02/14/when-should-a-compiler-expand-garbage-collection-barriers.html
saved…

Multi-Queue SSD I/O Modeling & Its Implications for Data Structure Design
Erin Ransom, Andrew Lim, Michael Mitzenmacher
https://arxiv.org/abs/2507.06349

Multi-Queue SSD I/O Modeling & Its Implications for Data Structure Design
Understanding the performance profiles of storage devices and how best to utilize them has always been non-trivial due to factors such as seek times, caching, scheduling, concurrent access, flash wear-out, and garbage collection. However, analytical frameworks that provide simplified abstractions of storage performance can still be accurate enough to evaluate external memory algorithms and data structures at the design stage. For example, the Disk Access Machine (DAM) model assumes that a stora…

Low Overhead Allocation Sampling in a Garbage Collected Virtual Machine
Christoph Jung, C. F. Bolz-Tereick
https://arxiv.org/abs/2506.16883 https://…

Low Overhead Allocation Sampling in a Garbage Collected Virtual Machine
Compared to the more commonly used time-based profiling, allocation profiling provides an alternate view of the execution of allocation heavy dynamically typed languages. However, profiling every single allocation in a program is very inefficient. We present a sampling allocation profiler that is deeply integrated into the garbage collector of PyPy, a Python virtual machine. This integration ensures tunable low overhead for the allocation profiler, which we measure and quantify. Enabling alloca…

Analysis of Server Throughput For Managed Big Data Analytics Frameworks
Emmanouil Anagnostakis, Polyvios Pratikakis
https://arxiv.org/abs/2506.03854 https:…

Analysis of Server Throughput For Managed Big Data Analytics Frameworks
Managed big data frameworks, such as Apache Spark and Giraph demand a large amount of memory per core to process massive volume datasets effectively. The memory pressure that arises from the big data processing leads to high garbage collection (GC) overhead. Big data analytics frameworks attempt to remove this overhead by offloading objects to storage devices. At the same time, infrastructure providers, trying to address the same problem, attribute more memory to increase memory per instance le…

this joke ⬇️
https://wetdry.world/@kopper/114694348090220778

🔄 Key features include automatic garbage collection, extendable frontend formats & concurrent dependency resolution
⚡ Efficient instruction caching with build cache import/export capabilities & nested build job invocations

Aaaaaaaaaaaa
I can save 60 bytes on a datastructure and get it optimized to make the entire pipeline for that specific case like up to 40% faster
It will now haunt me until I can work on it. Then I will be haunted by the large object garbage collector that also has significant optimization that are doable. Then the interpreter's GC and high level API will haunt me...
Can't I be like a normal person and be haunted by ghosts and specters instead of a night dwelling un…

WAMI: Compilation to WebAssembly through MLIR without Losing Abstraction
Byeongjee Kang, Harsh Desai, Limin Jia, Brandon Lucia
https://arxiv.org/abs/2506.16048

WAMI: Compilation to WebAssembly through MLIR without Losing Abstraction
WebAssembly (Wasm) is a portable bytecode format that serves as a compilation target for high-level languages, enabling their secure and efficient execution across diverse platforms, including web browsers and embedded systems. To improve support for high-level languages without incurring significant code size or performance overheads, Wasm continuously evolves by integrating high-level features such as Garbage Collection and Stack Switching. However, existing compilation approaches either lack…