The ever-growing need for high-performance computing has driven the popularity of employing multi-GPU systems. Modern multi-GPU systems employ unified virtual memory (UVM) to manage page placement and migration. However, the page management in UVM is application object agnostic. In this paper, we characterize the page access behaviors in relation to the application objects, and reveal that the beneficial page management policy varies according to (i) the different data objects within the same application, and (ii) the different execution phases of the same object.

Data races, which occur when two or more threads incorrectly access the same memory location without appropriate synchronization, cause CUDA programmers considerable pain. Even experts struggle to reason about extreme parallelism, multiple memory spaces and scopes, and diverse synchronization mechanisms. Races can manifest as subtle data corruption, deadlock, or livelock, which are difficult to reproduce in a debugging environment. To date, there is no generally reliable tool for identifying races in GPU programs at scale.

Rust has made safe systems programming practical on the CPU, but writing custom GPU kernels in Rust still forces programmers outside the language's ownership guarantees. We present cuTile Rust, a tile-based system for safe, idiomatic GPU kernel authoring in Rust that compiles kernels to Tile IR. cuTile Rust extends Rust's ownership discipline to tile-based GPU kernels: mutable outputs are split into disjoint pieces, kernel launches preserve the host-side ownership contract, and the Rust compiler enforces the same ownership rules inside the kernel.