인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Increasing simulation throughput is a major challenge for LHC experiments as they undergo significant detector upgrades for the high-luminosity phase. GPU-enabled particle transport simulation is a key R&D direction to address this, leveraging the growing availability of GPUs in computing centers. In its first phase, the AdePT project demonstrated that particle transport codes can be adapted for GPUs, integrated into a standard Geant4 workflow, and deliver significant speed-ups for standalone Geant4 setups of varying complexity. The second phase focuses on enabling seamless and efficient GPU usage within experiment frameworks via a Geant4 plugin. To achieve this, GPU transport kernels have been restructured into a header library hidden from the users, exposing only a configurable integration library easy to interface from diverse Geant4 applications. Several performance limitations identified in the first phase have been partially addressed. CPU-GPU scheduling has been improved to process multiple events on the GPU while allowing the CPU to perform asynchronous tasks. In addition, we continued the development of a new GPU-friendly surface-based geometry model, which mitigates some of the geometry-related bottlenecks. The initial integration of AdePT with two experiment frameworks has revealed challenges that will be addressed moving forward. Here, we present the latest results and insights, focusing on the hybrid Geant4-AdePT use case.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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