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www.postersession.com 1. We model faults that occur in GPU computation components, i.e. ALU, FPU and memory address calculation. 2. The categories of failures are: Crash, Silent Data Corruption (SDC), Hang and Benign GPUs have been originally designed for error-resilient workloads. Today, GPUs are used in error-sensitive applications. Evaluating Error Resilience of GPGPU Applications Bo Fang, Jiesheng Wei, Karthik Pattabiraman, Matei Ripeanu University of British Columbia Towards Building Error Resilient GPGPU Applications To reduce SDCs, we embed error detectors at program level based on heuristics and evaluate the efficacy of the error detectors by running the fault injection with 3 instrumented benchmarks Motivation Research Question Spot the 5 different pixels between these pictures! ATATTTTTTCTTGT TTTTTATATCCACA AACTCTTTTCGTA CTTTTACACAGTAT ATCGTGT Error The error (A->T) seriously affects the usability of the result Features: Breakpoint-based fault injection • On top of CUDA toolkit 4.1 (cuda-gdb 4.1) • On real GPU hardware Fault Injection Tool ATATTTTTTCTTGT TTTTTATATCCACA ATCTCTTTTCGTAC TTTTACACAGTATA TCGTGT Evaluation Results Overview of each benchmark Crash breakdown for each benchmark Take-aways: Crashes are the most frequent outcome SDCs (10%~40%) are much more common in GPGPU applications than in CPU applications The range of SDC popularity variation among applications is wider with GPGPU applications than with CPU applications Take-aways: Memory related crashes are the main reason of crashes Out-of-bound memory access dominates Better understand the fundamental reliability characteristics of GPGPU applications? • Specifically tolerance to transient faults in processing logic Methodology: • Design a fault injection tool to study the behavior of GPGPU applications under faults. Design Goals for the Fault Injection Tool 1. Has visibility to runtime information of executed instruction stream 2. Minimally interferes with the original GPGPU applications 3. Uniformly injects faults in dynamic instructions of applications Profiles one application Chooses one run-time instruction randomly Sets a conditional breakpoint and injects a fault 1 at run-time Collects failure results 2 Goal 1 Goal 3 Goal 2 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% AES MAT MUM BFS LIB Outcome percentage Benchmark program Crash Hang Silent Data CorrupGon Benign 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% AES MAT MUM BFS LIB Percentage of crash root cause Benchmark program Stack overflow Global outEofEbound Misaligned Local/share outEofEbound Experiment setup NVIDIA C2075 (Fermi architecture) 5 benchmarks Preliminary results 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% BFS MAT LIB Average SDC rate SDC rate with detectors Fault detection experimental results for instrumented benchmarks Contact: [email protected] Heuristic categories: Category I: Loop conditions. Category II: Branches with block or thread identifier Category III: Computation statements that pertain to: 1) Initialization of block and thread identifier 2) Computation involving block or thread id 3) Computation involving loop iterator variables 4) Data movement between global memory and other memory regions Error
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