The rise of asynchronous execution and specialized concurrent thread groups has reshaped GPU programming, but it also introduces complex timing and coordination challenges. Developers must carefully manage data readiness, concurrency, and hardware-specific tensor units that current low-level primitives make error-prone and hardware-dependent.

We present Async Graphene, a set of high-level abstractions for asynchronous and concurrent GPU programming. It builds on three pillars: (1) Async Tensor Types, which encode temporal relationships in the type system to prevent synchronization errors; (2) Concurrency Primitives, which structure complex control flow through specialization and pipelining; and (3) Tensor-Unit Orchestration, which simplifies low-level tensor hardware management.

On critical workloads such as GEMM and FlashAttention, Async Graphene delivers performance competitive with hand-optimized frameworks while significantly reducing development complexity. By making temporal behavior explicit, it allows compilers to address asynchrony and concurrency challenges without obscuring performance-critical details.