Accelerated Computational Drug Discovery via Heterogeneous Compute
Enabled research teams to execute complex molecular simulations at throughput levels exceeding traditional HPC environments, significantly reducing time-to-insight in early-stage drug discovery.
Situation
A research organization required scalable compute infrastructure to support protein modeling and molecular simulation workloads. Existing CPU-based clusters and conventional GPU pipelines were insufficient for the required simulation fidelity and iteration speed. Scientists faced high barriers to entry due to the complexity of parallel programming and infrastructure orchestration.
Solution
Designed and implemented a heterogeneous compute platform combining reconfigurable hardware and GPU acceleration. The platform emphasized hardware–software co-design, allowing computational models to be mapped directly onto optimized execution architectures rather than relying on generalized processors.
OUTCOMES
Challenges
Performance
- •Slow CPU clusters
- •Limited simulation throughput
Complexity
- •Difficult parallel programming
- •Infrastructure orchestration barriers
Scalability
- •Limited hardware flexibility
- •Constrained iteration velocity
Solutions
FPGA Acceleration Pipelines
Custom hardware acceleration pipelines using programmable logic.
- Implemented programmable logic for domain-specific compute acceleration
- Mapped simulation kernels directly onto optimized hardware paths
- Reduced dependency on generalized processor architectures
GPU Simulation Scaling
GPU-based parallel compute for high-throughput simulation workloads.
- Enabled massively parallel simulation execution across GPU clusters
- Accelerated protein modeling and molecular interaction workflows
- Improved throughput for early-stage discovery experimentation
- Supported scalable expansion of simulation capacity
Unified Simulation Abstraction
Hardware-agnostic simulation abstraction for scientific users.
- Abstracted heterogeneous infrastructure behind a unified interface
- Simplified simulation authoring for research teams
- Reduced reliance on specialist parallel computing knowledge
Kernel Offload Strategy
Co-processing strategies to offload critical computational kernels to optimized execution paths.
- Identified high-impact kernels for targeted hardware execution
- Improved efficiency of heterogeneous execution pipelines