
Learn to leverage Linux's kernel scheduler for writing soft real-time multithreaded applications in C, covering scheduling policies, thread-level control, and RTOS capabilities.

Explore multithreading techniques to optimize ray tracing performance. Learn how to implement parallel processing for faster rendering and improved efficiency in C++ ray tracing algorithms.

Optimize ray tracing performance through multithreading and code analysis. Learn techniques to identify bottlenecks and improve efficiency in C++ implementations for faster rendering.

Explore a proposed kernel driver solution to enhance Wine's performance by emulating NT synchronization primitives, potentially doubling speed for multithreaded applications like modern games.

Explore CPU isolation improvements using per-CPU spinlocks. Analyze performance costs, cache coherence, and contention. Compare with current solutions and discuss strategies for optimizing time-sensitive task execution.

Explore Flow Based Programming for IoT, addressing dynamic environments and concurrent I/O. Learn how this approach simplifies development and scales to microcontrollers using Soletta Project.

Explore coroutines in modern languages, their types, and implementation. Learn about stackless and stackfull variants, LLVM backend optimizations, and green threads in operating systems.

Explore process synchronization, race conditions, and the producer-consumer problem in operating systems. Learn key concepts for efficient multi-process management and coordination.

Explore XState for robust software development using state machines and actor models. Learn about state charts, AI integration, and practical applications while gaining insights into founding a tech startup.

Explore the Actor model with Orleans, Microsoft's implementation for creating distributed systems. Learn features and build a scalable pub crawl platform in this demo-heavy session.

Learn to write fast, parallel Julia code using Dagger.jl. Explore various programming interfaces, build example programs, and gain insights into parallelism through logging and profiling capabilities.

Explore a novel approach to dynamic deadlock prediction in concurrent systems, introducing sync-preserving deadlocks and efficient algorithms for sound, linear-time detection.

Explore concurrent mutation challenges and solutions in programming languages, focusing on innovative approaches to enhance system reliability and performance.

Explore a formally-verified framework for fair and abortable synchronization, enabling efficient implementation of various primitives with modular proofs and improved performance.

Explore a novel approach to memory management in concurrent programming, combining SMR algorithms with reference counting for efficient and safe automatic memory reclamation in unmanaged languages.

Explore a novel proof recipe for linearizability in relaxed memory, using object modification order to simplify event reordering and enable proof reuse in concurrent libraries.

Explore DisLog, a novel separation logic for verifying disentanglement in parallel programs. Learn about its applications, benefits, and case studies in concurrent programming.

Explore compiler optimization challenges in concurrent programs, focusing on memory models and safety. Investigate deriving models that retain optimization safety across different consistency levels.

Explore mind-boggling complexities of weak memory models in programming, delving into their intricacies and implications for software development.

Explore wait-free weak reference counting design techniques for efficient memory management in concurrent systems.

Explore LinearActris, a novel logic ensuring deadlock and leak freedom in message-passing concurrency, combining session types and separation logic for higher-order program verification.