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DTSTAMP:20181221T160731Z
LOCATION:C141/143/149
DTSTART;TZID=America/Chicago:20181115T133000
DTEND;TZID=America/Chicago:20181115T140000
UID:submissions.supercomputing.org_SC18_sess192_pap247@linklings.com
SUMMARY:Optimizing Software-Directed Instruction Replication for GPU Error
  Detection
DESCRIPTION:Paper\nAlgorithms, Architectures, GPUs, Linear Algebra, Networ
 ks, Resiliency, Tech Program Reg Pass\n\nOptimizing Software-Directed Inst
 ruction Replication for GPU Error Detection\n\nMahmoud, Hari, Sullivan, Ts
 ai, Keckler\n\nApplication execution on safety-critical and high-performan
 ce computer systems must be resilient to transient errors. As GPUs become 
 more pervasive in such systems, they must supplement ECC/parity for major 
 storage structures with reliability techniques that cover more of the GPU 
 hardware logic.  Instruction duplication has been explored for CPU resilie
 nce; however, it has never been studied in the context of GPUs, and it is 
 unclear whether the performance and design choices it presents makes it a 
 feasible GPU solution. This paper describes a practical methodology to emp
 loy instruction duplication for GPUs and identifies implementation challen
 ges that can incur high overheads (69% on average). It explores GPU-specif
 ic software optimizations that trade fine-grained recoverability for perfo
 rmance. It also proposes simple ISA extensions with limited hardware chang
 es and area costs to further improve performance, cutting the runtime over
 heads by more than half to an average of 30%.
URL:https://sc18.supercomputing.org/presentation/?id=pap247&sess=sess192
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