Search Results for "memory"

...Building on the principles of The Reactive Manifesto Akka allows you to write systems that self-heal and stay responsive in the face of failures. Up to 50 million msg/sec on a single machine. Small memory footprint; ~2.5 million actors per GB of heap. Distributed systems without single points of failure. Load balancing and adaptive routing across nodes. Event Sourcing and CQRS with Cluster Sharding. Distributed Data for eventual consistency using CRDTs. Asynchronous non-blocking stream processing with backpressure.

Apache Spark is a unified engine for large-scale data processing, offering APIs for batch jobs, streaming, machine learning, and graph computation. It builds on resilient distributed datasets (RDDs) and the newer DataFrame/Dataset abstractions to provide fault-tolerant, in-memory computation across clusters. Spark’s execution engine handles scheduling, shuffles, caching, and data locality so users can focus on transformations rather than infrastructure plumbing. With Spark Streaming (microbatches) and Structured Streaming, it delivers low-latency event processing suitable for real-time analytics. The built-in MLlib library provides scalable machine learning algorithms, while GraphX enables graph computations integrated with data pipelines. ...

...By being RESTful by default, including assets compilers, JSON & WebSocket support, Play is a perfect fit for modern web & mobile applications. Play is based on a lightweight, stateless, web-friendly architecture. Built on Akka, Play provides predictable and minimal resource consumption (CPU, memory, threads) for highly-scalable applications. Make your changes and simply hit refresh! All you need is a browser and a text editor. Underneath the covers Play uses a fully asynchronous model built on top of Akka.

...These range from tiny unit tests, to larger integration tests that instantiate a TestUtil.BaseModule in-process and a TestEvaluator to evaluate tasks on it. Note that the in-memory tests compile the BaseModule together with the test suite, and do not exercise the Mill script-file bootstrapping, transformation, and compilation process.

...The design targets modern performance goals—deep pipelines, speculative execution, multi-issue decode/execute, and sophisticated branch prediction—while remaining synthesizable for ASIC flows and portable to FPGAs for research. A modular microarchitecture separates frontend, backend, and memory subsystems with coherent caches and scalable interconnects, enabling multi-core configurations. The project invests heavily in verification: differential testing against reference models, extensive random instruction tests, and full software stacks (bootloaders, Linux) to validate correctness under realistic workloads. Tooling around the core (build scripts, simulators, waveform/debug support) lowers the barrier for academics and industry contributors to experiment and extend.

Algebird is Twitter’s Apache‑licensed Scala library providing abstract algebra data structures and algorithms, especially for online/streaming aggregation. It includes Monoid, Approximate, HyperLogLog, CMS, BloomFilter, Min/Max, Averaged Value types, supporting efficient distributed aggregation and approximate analytics.

FiloDB is an open-source distributed, real-time, in-memory, massively scalable, multi-schema time series / event / operational database with Prometheus query support and some Spark support as well. The normal configuration for real-time ingestion is deployment as stand-alone processes in a cluster, ingesting directly from Apache Kafka. The processes form a cluster using peer-to-peer Akka Cluster technology.

...A diplomacy framework (LazyModules) lets designers wire components with negotiated parameters, enabling reuse and rapid exploration of different cache sizes, port counts, and memory hierarchies. The generator supports custom accelerators through the RoCC interface, allowing domain-specific compute units to be plugged into the pipeline with shared cache and memory semantics. Tooling integrates with FIRRTL, Verilator, and commercial EDA flows, and the ecosystem around Rocket Chip (e.g., Chipyard) adds harnesses, peripherals, and verification infrastructure.

Vale is a systems programming language and compiler that strives to combine performance, safety, and ease in a way that avoids many of the complexities of traditional memory management techniques. It is ahead-of-time (AOT) compiled, targeting LLVM as a backend, and is statically typed. The language introduces a concept called generational references to provide memory safety without relying on a garbage collector, and aims for “safe without a borrow checker” (i.e. reducing the burden on the programmer). ...

The riscv-boom project (also called BOOM or SonicBOOM) implements a high-performance, synthesizable out-of-order RISC-V core written in the Chisel hardware construction language. It targets the RV64GC (i.e. 64-bit with general + compressed + floating point) instruction set and supports features such as virtual memory, caches, atomics, and IEEE-754 floating point. The design is parameterizable, meaning users can tune pipeline widths, buffer sizes, functional units, and other microarchitectural knobs to explore tradeoffs. It is capable of booting Linux and running standard benchmarks, and its performance (measured in CoreMarks/MHz) is competitive with commercial cores. ...