고성능 컴퓨팅 플랫폼

Central Processing Units (CPUs) and Graphics Processing Units (GPUs) are arguably the two most widely deployed processors available on most desktops, servers, and mobile devices. Still, their architectures continue to change as their primary target workloads change over time. In this research, we design and propose next-generation CPU and GPU architectures to accommodate the ever-increasing performance demands of emerging CPU and GPU workloads. We pursue both software- and hardware-oriented approaches by prototyping software-based performance optimizations on real CPUs and GPUs, and by evaluating architectural extensions using cycle-level simulators such as gem5 and GPGPU-Sim. Examples of this research topic include LATPC [MICRO '25], GPUdmm [HPCA '14], and ScaleGPU [CAL '14]. Database management systems (DBMSs; e.g., MySQL, MonetDB) are one of the most important server workloads as they allow users to capture and analyze large amounts of data. Fast query processing is a key design goal of DBMSs, and we seek to achieve the goal through hardware acceleration. In this research, to accelerate query processing, we analyze the key performance bottlenecks of the existing DBMSs and propose hardware acceleration techniques that fully exploit the high potential of modern hardware. Example outcomes of this topic are PID-Join [SIGMOD '23], a fast in-memory join algorithm exploiting the processing-in-memory capabilities of commodity DIMM devices, and SPID-Join [SIGMOD '25], a skew-resistant in-memory join algorithm that achieves high skew resistance by leveraging the architectural characteristics of commodity processing-in-DIMM devices. Additional example outcomes include FaScalSQL [ICDE '26], a fast and scalable GPU-accelerated analytical query engine for out-of-memory tables, and