This paper presents a zero-overhead, multi-vendor GPU compilation framework built natively into the Rust compiler (rustc) and LLVM backends, and leverages Rust's rich type system, ownership system, and strict aliasing guarantees to efficiently manage and optimize data transfers through LLVM's Offload infrastructure.
Abstract
High-performance GPU programming has traditionally forced a compromise between execution efficiency and memory safety. While Rust guarantees compile-time memory safety for host CPUs via its strict ownership model, applying these constraints to massively parallel GPU execution environments has previously mandated either vendor-locked Domain-Specific Languages (DSLs) or escaping to explicit unsafe raw pointers. This paper presents a zero-overhead, multi-vendor GPU compilation framework built natively into the Rust compiler (rustc) and LLVM backends. We leverage Rust's rich type system, ownership system, and strict aliasing guarantees (noalias) to efficiently manage and optimize data transfers through LLVM's Offload infrastructure. We expose the technical challenges of cross-vendor ABI lowering mismatches between Host and Device targets and introduce a two-pass compilation pipeline capable of safely handling both manual and compiler-generated memory movements. Evaluating our framework on RAJAPerf demonstrates that our rustc-based solution can generate competitive LLVM IR for GPU kernels, achieving a solid kernel performance against native, hand-optimized CUDA and HIP C++ baselines.
Ghost is an OS-level GPU virtualization layer integrated directly into the open-source GPU driver, using a GPU container abstraction with cgroup -like APIs for compute and memory control and privileged hardware-level scheduling and preemption for dynamic compute resource management.
WarpGuard is presented, the first composite CFA framework for heterogeneous CPU-GPU workloads, and enables runtime CFA of GPU kernels by tracing their execution against kernel-specific CFGs, and it monitors kernel launch events and enforces per-call site policies to detect violations at the CPU-GPU boundary.
Christian Lindenmeier, Meni Orenbach, Amro Awad et al.· arXiv.org· 0 citations
This work introduces Backline, a heterogeneous compilation and runtime framework built within PennyLane and Catalyst that allows researchers to write workloads in high-level languages that map to low-latency execution across diverse distributed target platforms, and demonstrates the compilation and execution of several...
Joseph K. L. Lee, M. Malekmohammadi, Hong-Sheng Zheng et al.· 0 citations
A structured, paired comparison of configuration complexity and performance across six platforms (Proxmox VM and LXC, native KVM, OpenStack VM and Zun, and Podman) on a single NVIDIA RTX 4500 Ada GPU, emphasizing the under-documented container paths
Priska Steininger, Manfred Pamsl, Helmut Lindner et al.· Computers· 0 citations
LazyTrain is proposed, an optimization layer over a layer-streaming executor that formulates checkpoint selection, activation placement, recomputation, and CPU-GPU-NVMe communication overlap as a mixed-integer scheduling problem, then executes the solved policy during training.
Xiao-Jun Wu, Ce-Hao Yang, Hong-Hao Liu et al.· 1 citation
Serving a large language model (LLM) is limited by memory capacity. High-Bandwidth Flash (HBF) stacks NAND flash inside the accelerator package, one tier below high-bandwidth memory (HBM); the specification was published on August 3, 2026, and the first inference devices are expected to sample in early 2027. Decisions...
Unknown authors· 0 citations
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