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Limits: FPGA/AMD toolchain과 평가 클러스터 규모에 제약되며 초록에 정확한 수치가 없다.</property:Applicability>
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고정 하드웨어 collective는 재합성이 필요하고 CPU MPI는 통신·CPU 비용이 크다.

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Source: https://arxiv.org/abs/2312.11742
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Canonical title: ACCL+: an FPGA-Based Collective Engine for Distributed Applications
Question: 분산 애플리케이션의 collective 통신을 FPGA에서 범용·확장 가능하게 가속할 수 있는가?
Context: 고정 하드웨어 collective는 재합성이 필요하고 CPU MPI는 통신·CPU 비용이 크다.
Method: ACCL+는 UDP/TCP/RDMA, FPGA 직접 사용과 CPU offload, 재합성 없는 확장성을 제공한다.
Evaluation: workloads=100 Gb/s FPGA cluster; CPU vector-matrix multiply; FPGA DLR inference; baselines=software MPI; software RDMA collectives; metrics=collective performance; application performance; results=Significant/competitive gains reported; exact aggregate not abstract-verified.
Interpretation: collective를 재사용 가능한 FPGA 네트워크 엔진으로 분리하면 여러 실행 모델을 지원할 수 있다.
Reusable lesson: 가속기 통신은 특정 앱 회로보다 프로토콜·collective 계층의 재사용성을 설계하라.
Applicability: FPGA 클러스터 HPC·분산 추론.
Limits: FPGA/AMD toolchain과 평가 클러스터 규모에 제약되며 초록에 정확한 수치가 없다.</property:Evidence_note>
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Canonical title: ACCL+: an FPGA-Based Collective Engine for Distributed Applications
Question: 분산 애플리케이션의 collective 통신을 FPGA에서 범용·확장 가능하게 가속할 수 있는가?
Context: 고정 하드웨어 collective는 재합성이 필요하고 CPU MPI는 통신·CPU 비용이 크다.
Method: ACCL+는 UDP/TCP/RDMA, FPGA 직접 사용과 CPU offload, 재합성 없는 확장성을 제공한다.
Evaluation: workloads=100 Gb/s FPGA cluster; CPU vector-matrix multiply; FPGA DLR inference; baselines=software MPI; software RDMA collectives; metrics=collective performance; application performance; results=Significant/competitive gains reported; exact aggregate not abstract-verified.
Interpretation: collective를 재사용 가능한 FPGA 네트워크 엔진으로 분리하면 여러 실행 모델을 지원할 수 있다.
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Applicability: FPGA 클러스터 HPC·분산 추론.
Limits: FPGA/AMD toolchain과 평가 클러스터 규모에 제약되며 초록에 정확한 수치가 없다.</property:Evidence_note>
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