1 / 11

Enhanced GPU Computing with Condor at The Hartford: Achieving Rapid Actuarial Insights

During Condor Week 2012, Bob Nordlund discussed The Hartford's journey in optimizing GPU computing within their production environment, implemented since 2004. Leveraging approximately 7,000 cores, the focus was on reducing computation time for critical actuarial tasks, such as market sensitivity analysis and hedging simulations. The integration of CUDA, optimized algorithms, and innovative modeling strategies resulted in significant performance improvements and cost reductions. The ambitious goals include real-time analytics and enhanced infrastructure capabilities, setting the stage for future advancements.

roz
Download Presentation

Enhanced GPU Computing with Condor at The Hartford: Achieving Rapid Actuarial Insights

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. GPU Computing with Condor @The Hartford Condor Week 2012 Bob Nordlund

  2. Grid Computing @The Hartford… • Using Condor in our production environment since 2004 • Computing Environment • Two pools (Hartford, CT and Boulder, CO) • Linux central managers and schedulers • Windows execute nodes (~7000 cores) • CycleServer from Cycle Computing LLC • Workload • Mix of off-the-shelf tools and in-house custom software • Actuarial modeling • Financial reporting • Compliance • Enterprise risk management • Hedging • Stress testing

  3. The Challenge… • Compress the time it takes to compute market sensitivities to enable rapid response to large market movements • Current compute time: ~8 hours on ~3000 cores • Target: A.S.A.P.(P being practical) • Compress the time it takes to simulate our hedging program • Current compute time: ~5 days on ~5000 cores • Target: 1 day • Create a mechanism to calculate specific sensitivities in near real time • Support Entire Model Portfolio: ~20 models • Maintain Accuracy and Precision • Enterprise IT Targets • Reduce Datacenter Footprint • Reduce Costs

  4. The Approach…(everything’s on the table) • Modeling • Variance Reduction • Optimize algorithms • Eliminate Redundant or Un-necessary Work • Processes • Optimize submission pipeline • Reduce file transfers • Implement Master/Worker framework • Models • Optimize code • Caching • Dynamic scenario generation • CUDA/OpenCL/OpenMP • Infrastructure • Improve storage • GPUs

  5. The Plan… • Modeling • Test convergence with low-discrepancy sequences • Evaluate closed-form or replicating portfolio approach • Remove un-necessary workload • Processes • Interleave scenario/liability/asset submissions • Improve nested stochastic analysis • Develop Master/Worker scheduling • Models • Port model portfolio to CUDA • Optimize algorithms • Purchase GPU Infrastructure • 250 NVIDIA Tesla 2070s

  6. The Results… • Modeling • Convergence achieved faster with low-discrepancy sequences • 2x improvement • Removed non-essential tasks • 2x improvement • Processes • Streamlined submission pipeline for scenario/liability/assets • Eliminated ~1TB/run of file transfer • Using Work Queue for Master/Worker • 4-6x improvement

  7. The Results (cont.)… • Models • Developed code generator for CUDA • Automated development and end-user automation (priceless!) • Directly Compiled Spec Models • Ported entire model portfolio to CUDA (GPU) and C++ (CPU) • 40-60x improvement • Infrastructure • 125 Servers with 250 M2070s • 3x reduction in data center footprint • 50% cost reduction • Summary • Success! • Improved Performance • Reduced Cost • Improved our long-term capabilities

  8. What’s Next? Complete integration of GPUs into our Condor environment • Quickly find the GPU nodes • GPU = “None” • SLOT1_GPU =“NVIDIA” • SLOT2_GPU=“NVIDIA” • STARTD_EXPRS = $(STARTD_EXPRS), GPU • Identify GPGPU submissions • +GPGPU=True • Reserve Slots for GPGPU jobs • START=( ( ( SlotID < 3 ) && ( GPUGPU =?= True ) ) || ( (SlotID > 2) && (GPGPU =!= True) ) ) • Work with Todd on GPU wish list • Benchmarking • Monitoring (corrupt memory, etc.)

  9. What’s Next?Refine our job scheduling architecture • Minimize Scheduling Overhead • Continue development on our Work Queue implementation • Leverage new Condor features – key_claim_idle? • Optimize Work Distribution • Need to prevent starvation of fast GPU resources while still leveraging existing dedicated and scavenged CPUs • Integrate with CycleServer • High-availability/disaster recovery • Persistent queues • Support for multiple resource pools

  10. What’s Next?Expand Condor’s footprint @The Hartford • Condor for Server Utilization Monitoring • Install Condor on all servers • Improved reporting and, • Foot-in-the-door for scavenging! • Condor in the Cloud • Condor Interoperability (MS HPC Server) • Evangelize Condor to ISVs

  11. Thank you! Bob Nordlund Enterprise Risk Management Technology The Hartford robert.nordlund@thehartford.com

More Related