Simultaneous Branch and Warp Interweaving for Sustained GPU Performance

International audienceSingle-Instruction Multiple-Thread (SIMT) micro-architectures implemented in Graphics Processing Units (GPUs) run fine-grained threads in lockstep by grouping them into units, referred to as warps, to amortize the cost of instruction fetch, decode and control logic over multiple execution units. As individual threads take divergent execution paths, their processing takes place sequentially, defeating part of the efficiency advantage of SIMD execution. We present two complementary techniques that mitigate the impact of thread divergence on SIMT micro-architectures. Both techniques relax the SIMD execution model by allowing two distinct instructions to be scheduled to disjoint subsets of the the same row of execution units, instead of one single instruction. They increase flexibility by providing more thread grouping opportunities than SIMD, while preserving the affinity between threads to avoid introducing extra memory divergence. We consider (1) co-issuing instructions from different divergent paths of the same warp and (2) co-issuing instructions from different warps. To support (1), we introduce a novel thread reconvergence technique that ensures threads are run back in lockstep at control-flow reconvergence points without hindering their ability to run branches in parallel. We propose a lane shuffling technique to allow solution (2) to benefit from inter-warp correlations in divergence patterns. The combination of all these techniques improves performance by 23% on a set of regular GPGPU applications and by 40% on irregular applications, while maintaining the same instruction-fetch and processing-unit resource requirements as the contemporary Fermi GPU architecture

MedPerf : Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation

Medical AI has tremendous potential to advance healthcare by supporting the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving provider and patient experience. We argue that unlocking this potential requires a systematic way to measure the performance of medical AI models on large-scale heterogeneous data. To meet this need, we are building MedPerf, an open framework for benchmarking machine learning in the medical domain. MedPerf will enable federated evaluation in which models are securely distributed to different facilities for evaluation, thereby empowering healthcare organizations to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status, and our roadmap. We call for researchers and organizations to join us in creating the MedPerf open benchmarking platform