There is a growing interest in the computer architecture community to incorporate heterogeneity and specialization to improve performance. Developers can create heterogeneous applications that consist of both host code and kernel code, where compute-intensive kernels can be offloaded from CPU to hardware accelerators. Testing such applications on real heterogeneous architectures is extremely challenging as kernels are black boxes, providing no information about the kernels’ internal execution to diagnose issues such as silent hangs or unexpected results. Additionally, inputs for heterogeneous applications are often large matrices, leading to a vast search space for identifying bug-revealing inputs.

We propose a novel fuzz testing technique, HFuzz, to enable efficient testing on real heterogeneous architectures. HFuzz aims to increase both the observability of hardware kernels and testing efficiency through a three-pronged approach. First, HFuzz automatically generates test guidance by inserting device-side in-kernel hardware probes in addition to host-side software monitors. Second, it performs rapid input space exploration by offloading compute-intensive input mutations to hardware kernels. Third, HFuzz parallelizes fuzzing and enables fast on-chip memory access, by utilizing four FPGA-level optimizations including loop unrolling, shannonization, data preloading, and dynamic kernel sharing.

We evaluate HFuzz on seven open-source OneAPI subjects from Intel. HFuzz speeds up fuzz testing by 4.7× with HW-accelerated input space exploration. By incorporating HW probes in tandem with SW monitors, HFuzz finds 33 defects within 4 hours and reveals 25 unique, unexpected behavior symptoms that could not be found by SW-based monitoring alone. HFuzz is the first to design hardware optimizations to accelerate fuzz testing.