Hybrid CPU/GPU/APU accelerated query, insert, update and erase operations in hash tables with string keys

Abstract

Modern computer systems can use different types of hardware acceleration to achieve massive performance improvements. Some accelerators like FPGA and dedicated GPU (dGPU) need optimized data structures for the best performance and often use dedicated memory. In contrast, APUs, which are a combination of a CPU and an integrated GPU (iGPU), support shared memory and allow the iGPU to work together with the CPU on pointer-based data structures. First, we develop an approach for dGPU to accelerate queries in libcuckoo and robin-map and when looking at accelerating insert, updates and erase operations in the original libcuckoo using OneAPI on an APU. We evaluate the dGPU against the CPU variants and our dGPU approach adapted for the CPU and also in a hybrid context by using longer keys on the CPU and shorter keys on the dGPU. In comparison with the original libcuckoo algorithm, our dGPU approach achieves a speed-up of 2.1, and in comparison with the robin-map a speed-up of 1.5. For hybrid workloads, our approach is efficient if long keys are processed on the CPU and short keys are processed on the dGPU. By processing a mixture of 20% long keys on the CPU and 80% short keys on dGPU, our hybrid approach has a 40% higher throughput than the CPU only approach. In addition, we develop a hybrid APU approach for insert, update and erase operations in the original libcuckoo structure focusing on shared memory with iGPU accelerated look-ups of the positions for insert, update and erase operations.

Multi-Pattern GPU Accelerated Collision-Less Rabin-Karp for NIDS

“…Additionally, this architecture minimizes memory latency by consolidating all data in shared and global memory, contrasting with CPU only which incurs latency when loading/unloading data. This strategic choice enhances the algorithm's overall performance on parallel architectures as the recent article published by (Groth et al, 2023) in the knowledge and information systems journal has proved to be 40% higher throughput than the CPU-only approach when dealing with string manipulation operations. Our approach offers a considerable speedup, especially when dealing with a large data set of patterns as mentioned in the 2023 study published Application of soft computing (Baloi et al, 2023) which used GPU-accelerated pattern matching to find similarity metrics.…”