high performance computing on graphics processing units: hgpu.org

Data stream processing applications such as stock exchange data analysis, VoIP streaming, and sensor data processing pose two conflicting challenges: short per-stream latency — to satisfy the milliseconds-long, hard real-time constraints of each stream, and high throughput — to enable efficient processing of as many streams as possible. High-throughput programmable accelerators such as modern GPUs hold high potential to speed up the computations. However, their use for hard real-time stream processing is complicated by slow communications with CPUs, variable throughput changing non-linearly with the input size, and weak consistency of their local memory with respect to CPU accesses. Furthermore, their coarse grain hardware scheduler renders them unsuitable for unbalanced multi-stream workloads. We present a general, efficient and practical algorithm for hard real-time stream scheduling in heterogeneous systems. The algorithm assigns incoming streams of different rates and deadlines to CPUs and accelerators. By employing novel stream schedulability criteria for accelerators, the algorithm finds the assignment which simultaneously satisfies the aggregate throughput requirements of all the streams and the deadline constraint of each stream alone. Using the AES-CBC encryption kernel, we experimented extensively on thousands of streams with realistic rate and deadline distributions. Our framework outperformed the alternative methods by allowing 50% more streams to be processed with provably deadline-compliant execution even for deadlines as short as tens milliseconds. Overall, the combined GPU-CPU execution allows for up to 4-fold throughput increase over highly-optimized multi-threaded CPU-only implementations.