<p>Efficiently predicting aftershocks based on rupture dynamics simulation is a crucial task in high-performance computing, traditionally dependent on supercomputers. However, the constraints of power supply makes supercomputers impractical right after a primary earthquake event. This positions FPGAs, known for their high power efficiency and reconfigurability, as a highly promising alternative. Within rupture dynamics simulation, fault interaction is the most computationally intensive component, making its acceleration crucial for enhancing overall performance. By thoroughly considering both the computational characteristics of fault interaction and the reconfigurable capabilities of FPGAs, we have devised a high-performance, power-efficient accelerator for fault interaction. On one hand, we conduct an in-depth analysis of the algorithm’s data dependencies and exploit parallelization at multiple levels to maximize performance. On the other hand, we propose novel dataflow optimizations, such as prefetching and overlapping computations across different stages, to further enhance efficiency. Additionally, we implement a latency-matching strategy and a flag-based mechanism to ensure seamless coordination between computational stages. Experimental results demonstrate the superior efficiency of our FPGA accelerator across geological models. Against a 12-core Intel Xeon CPU, the FPGA achieves 12.3<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11227_2025_7801_Article_IEq1.gif" Format="GIF" Height="13" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">\(\times\)</EquationSource> <EquationSource Format="MATHML"><math> <mo>×</mo> </math></EquationSource> </InlineEquation> speedup and 27.1<InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11227_2025_7801_Article_IEq1.gif" Format="GIF" Height="13" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">\(\times\)</EquationSource> <EquationSource Format="MATHML"><math> <mo>×</mo> </math></EquationSource> </InlineEquation> energy efficiency for the uniform Basin Model, delivering 1.8<InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11227_2025_7801_Article_IEq1.gif" Format="GIF" Height="13" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">\(\times\)</EquationSource> <EquationSource Format="MATHML"><math> <mo>×</mo> </math></EquationSource> </InlineEquation> the energy efficiency of the contemporary RTX 2080 GPU. For the irregular Mountain Model, it delivers 15.4<InlineEquation ID="IEq4"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11227_2025_7801_Article_IEq1.gif" Format="GIF" Height="13" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">\(\times\)</EquationSource> <EquationSource Format="MATHML"><math> <mo>×</mo> </math></EquationSource> </InlineEquation> speedup and 35.8<InlineEquation ID="IEq5"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11227_2025_7801_Article_IEq1.gif" Format="GIF" Height="13" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">\(\times\)</EquationSource> <EquationSource Format="MATHML"><math> <mo>×</mo> </math></EquationSource> </InlineEquation> energy efficiency over the same CPU, exceeding the RTX 4080 by 5.0<InlineEquation ID="IEq6"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11227_2025_7801_Article_IEq1.gif" Format="GIF" Height="13" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">\(\times\)</EquationSource> <EquationSource Format="MATHML"><math> <mo>×</mo> </math></EquationSource> </InlineEquation> in efficiency while approaching its computational throughput.</p>

错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

An FPGA-based efficient accelerator for fault interaction of rupture dynamics

  • Ming Yuan,
  • Qiang Liu,
  • Lin Gan

摘要

Efficiently predicting aftershocks based on rupture dynamics simulation is a crucial task in high-performance computing, traditionally dependent on supercomputers. However, the constraints of power supply makes supercomputers impractical right after a primary earthquake event. This positions FPGAs, known for their high power efficiency and reconfigurability, as a highly promising alternative. Within rupture dynamics simulation, fault interaction is the most computationally intensive component, making its acceleration crucial for enhancing overall performance. By thoroughly considering both the computational characteristics of fault interaction and the reconfigurable capabilities of FPGAs, we have devised a high-performance, power-efficient accelerator for fault interaction. On one hand, we conduct an in-depth analysis of the algorithm’s data dependencies and exploit parallelization at multiple levels to maximize performance. On the other hand, we propose novel dataflow optimizations, such as prefetching and overlapping computations across different stages, to further enhance efficiency. Additionally, we implement a latency-matching strategy and a flag-based mechanism to ensure seamless coordination between computational stages. Experimental results demonstrate the superior efficiency of our FPGA accelerator across geological models. Against a 12-core Intel Xeon CPU, the FPGA achieves 12.3 \(\times\) × speedup and 27.1 \(\times\) × energy efficiency for the uniform Basin Model, delivering 1.8 \(\times\) × the energy efficiency of the contemporary RTX 2080 GPU. For the irregular Mountain Model, it delivers 15.4 \(\times\) × speedup and 35.8 \(\times\) × energy efficiency over the same CPU, exceeding the RTX 4080 by 5.0 \(\times\) × in efficiency while approaching its computational throughput.