We propose the applicationaware noc aanoc management to better exploit the application. This paper uses hardware thread scheduling to improve the performance and energy efficiency of divergent applications on gpus. Nov 27, 2017 it has become an important factor affecting the performance of gpgpus. Extracting relevant fragments from software development video tutorials. We propose a specialized cache management policy for gpgpus.
Localityprotected cache allocation scheme with low. Existing cpu cache management policies that are designed for multicore systems, can be suboptimal when directly applied to gpu caches. Daws attempts to shift the burden of locality management from software to hardware, increasing the performance of simpler and more portable code on the gpu. It has become an important factor affecting the performance of gpgpus. This dissertation proposes three novel gpu microarchitecture enhancements for mitigating both the locality and utilization problems on an important class of irregular gpu applications. Memory divergence aware gpu cache management the 29th international conference on supercomputing ics15 january 1, 2015 eliminating intrawarp conflict misses in gpu.
In this paper, we propose a divergence aware cache dacache management that can orchestrate l1d cache management and warp scheduling together for gpgpus. Instruction and memory divergence based cache management for gpus. Chulian zhang senior electrical engineer microsoft. Tim rogers divergenceaware warp scheduling 4 motivation transfer locality management from sw to hw software solutions. Proceedings of the international conference on supercomputing ics, pp 8998.
Gpu applications using software tools, such as autotuners 82, 89, 172, 295, 311, optimizing. Cuda memory and cache architecture the supercomputing blog. Exploiting interwarp heterogeneity to improve gpgpu. Memory divergence or an uncoalesced memory access occurs when. Based on this discovery we argue that cache management must be done using perload locality type information, rather than applying warpwide cache management policies. Prior work on memory scheduling for gpus has dealt with a single application context only.
Were upgrading the acm dl, and would like your input. Publications scs lab illinois institute of technology. We propose a divergence aware cache management technique, namely dacache, to orchestrate warp scheduling and cache management for gpgpus. Dimensionalityaware redundant simt instruction elimination. The throughputoriented execution model in gpu introduces thousands of hardware threads, which may access the small cache simultaneously. Interwarp divergence aware execution on gpus northeastern. Applicationaware noc management in gpus multitasking. Gpu enenrgy efficiency through software hardware codesign. A survey of architectural approaches for improving gpgpu. Locality based warp scheduling in gpgpus sciencedirect. To avoid oversubscribing the cache, divergenceaware warp scheduling prevents warp 1 from entering the loop by descheduling it. In this paper, we put forward a coordinated warp scheduling and locality protected cwlp cache allocation scheme to make full use of data locality and. Power efficient sharingaware gpu data management alchem. View tor aamodts profile on linkedin, the worlds largest professional community.
Locality and scheduling in the massively multithreaded era. Mitigating gpu memory divergence for dataintensive. In proceedings of the 2016 ieee international symposium on workload characterization, iiswc 2016 pp. Localityprotected cache allocation scheme with low overhead.
Dec 07, 20 divergence aware warp scheduling uses the information gathered from warp 0 to predict that the data loaded by warp 1s active threads will evict data reused by warp 0 which is still in the loop. Exploiting interwarp heterogeneity to improve gpgpu performance. If one instruction fails to hit on chip, the requests are pushed to l2 cache in the memory partition through memory port and interconnect. We propose access patternaware cache management apcm, which dynamically detects the locality type of each load instruction by monitoring the accesses from one exemplary warp. Us patent for continuation analysis tasks for gpu task. Improve performance of programs with memory divergence.
Exploring hybrid memory for gpu energy efficiency through softwarehardware codesign. Second, the massively multithreaded gpu architecture presents significant storage overheads for buffering thousands of inflight coherence requests. Our evaluations show that the gpuaware cache and memory management techniques proposed in this dissertation are effective at mitigating the interference caused by. Jan 25, 2019 wang b, yu w, sun xh, wang x 2015 dacache. Orchestrating cache management and memory scheduling for gpgpu applications by jie slides a scalable multipath microarchitecture for efficient gpu control flow by shuwen slides. When memory divergence occurs, a warp incurs up to warp. Parallel architecture and system research lab pasl at auburn. Highperformance and energyeffcient memory scheduler design for heterogeneous systems. We propose divergence aware warp scheduling daws, which introduces a divergence based cache footprint predictor to estimate how much l1 data cache capacity is needed to capture intrawarp locality in loops. This paper proposes to tightly couple the thread scheduling mechanism with the cache management algorithms such that gpu cache pollution is minimized while offchip memory throughput is enhanced.
In addition, warp scheduling is very important for gpuspecific cache management to reduce both intra and interwarp conflicts and maximize data locality. Divergenceaware warp scheduling uses the information gathered from warp 0 to predict that the data loaded by warp 1s active threads will evict data reused by warp 0 which is still in the loop. In proceedings of the 29th acm on international conference on supercomputing, ics15, newport beachirvine, ca, usa, june 08 11, 2015 pp. Then, we will present a divergenceaware cache management that can orchestrate l1d cache management and warp scheduling together for gpgpus.
The university of british columbia curriculum vitae for. Divergenceaware warp scheduling ieee conference publication. In addition, warp scheduling is very important for gpuspecific cache management to reduce both intra and interwarp. Cta scheduling, memory placement, cache management, and prefetching. Sung, reducing offchip memory traffic by selective cache management scheme in gpgpus, 5th annual workshop on general purpose processing with graphics processing units acm, 2012 pp. Highperformance and energyeffcient memory scheduler. Current networkonchip noc designs in gpus are agnostic to application requirements, and this leads to wasted performance in gpus multitasking. Developing a resilience testbed for vulnerability analysis and hil testing of electrical grids.
We propose memoryaware tlp throttling and cache bypassing matb mechanism, which can exploit data temporal locality and memory bandwidth. Fang zhou, hai pham, jianhui yue, hao zou and weikuan yu. Cache coherence protocol design for active memory systems. Exploiting spatial locality, micro 98 a localityaware memory hierarchy for energyefficient gpu arch, micro 99 divergenceaware warp scheduling, micro 100 linearly compressed pages.
Tor aamodt is a professor in the department of electrical and computer engineering at the university of british columbia where he has been a faculty member since 2006. Cuda memory and cache architecture understanding the basic memory architecture of whatever system youre programming for is necessary to create high performance applications. Association for computing machinery resource library. Bin wang software engineer arista networks linkedin. This cited by count includes citations to the following articles in scholar. We propose access pattern aware cache management apcm, which dynamically detects the locality type of each load instruction by monitoring the accesses from one exemplary warp. Abstractthe power consumed by memory system in gpus is a significant fraction of. It aims to make those cache blocks with good data locality stay inside l1d cache longer while maintaining onchip resources utiliza tion. Exploiting spatial locality, micro 98 a locality aware memory hierarchy for energyefficient gpu arch, micro 99 divergence aware warp scheduling, micro 100 linearly compressed pages.
Divergenceaware warp scheduling ubc ece university of. On the gpu, each thread has access to shared or local memory, which is analogous to cache on the cpu. Third, these protocols increase the verification complexity of the gpu memory system. To avoid oversubscribing the cache, divergence aware warp scheduling prevents warp 1 from entering the loop by descheduling it. The purpose was to guide scheduling decisions to improve the locality in the access stream of memory references seen by the cache. We propose a divergenceaware cache management technique, namely dacache, to orchestrate warp scheduling and cache management for gpgpus. Ics 2002 daehyun kim, mainak chaudhuri, and mark heinrich.
We propose memory aware tlp throttling and cache bypassing matb mechanism, which can exploit data temporal locality and memory bandwidth. View chulian zhangs profile on linkedin, the worlds largest professional community. A tagless cache for reducing energy, micro 97 decoupled compressed cache. Our evaluations show that the gpu aware cache and memory management techniques proposed in this dissertation are effective at mitigating the interference caused by gpus on current and future gpu. First, gpudmm simplifies the gpgpu programming by relieving the programmer of cpugpu memory management burden. We propose divergenceaware warp scheduling daws, which introduces a divergencebased cache footprint predictor to estimate how much l1 data cache capacity is needed to capture intrawarp locality in loops. Anatomy of gpu memory system for multiapplication execution. Professor tor aamodt computer architecture including accelerators for deep neural networks and architecture of graphics processor units for nongraphics computing. Citeseerx document details isaac councill, lee giles, pradeep teregowda. Most desktop systems consist of large amounts of system memory connected to a single cpu, which may have 2 or three levels or fully coherent cache. Memoryaware tlp throttling and cache bypassing for gpus. Dacache proceedings of the 29th acm on international. See the complete profile on linkedin and discover chulians.
This will cause cache thrashing and contention problems and limit gpu performance. In one embodiment, a continuation packet is referenced directly by a first task. Mitigating gpu memory divergence for dataintensive applications. We evaluate caching effectiveness of gpu data caches for both memorycoherent and memorydivergent gpgpu benchmarks, and present the problem of partial caching in existing gpu cache management. Even though the impacts of memory divergence can be alleviated through various software techniques, architectural support for memory divergence mitigation is still highly desirable to ease the complexity in the programming and optimization of gpu accelerated dataintensive applications. This helps limit the number of accesses made to the skip pc table each cycle. In proceedings of the 2002 international conference on parallel and distributed processing techniques and applications, pages 8389, june 2002. We demonstrate that the predictive, preemptive nature of daws can provide an additional 26% performance improvement over ccws.
We propose divergenceaware warp scheduling daws, which introduces a divergencebased cache footprint predictor to estimate how much l1 data cache capacity is. Highperformance and energyeffcient memory scheduler design. The first mechanism, cache conscious warp scheduling ccws, is an adaptive hardware mechanism that makes use of a novel locality detector to capture memory. Improving performance of parallel io systems through selective and layoutaware ssd cache ieee transactions on parallel and distributed systems tpds, vol. Gpudmm enables dynamic memory management for discrete gpu environments by using gpu memory as a cache of cpu memory with ondemand cpugpu data transfers. In this paper, we propose a divergenceaware cache da. Improving performance of parallel io systems through selective and layout aware ssd cache ieee transactions on parallel and distributed systems tpds, vol. Memory divergenceaware gpu cache management the 29th international conference on supercomputing ics15 january 1, 2015 eliminating intrawarp conflict misses in gpu. Economically important server computing, big data tim rogers. In addition, warp scheduling is very important for gpu specific cache management to reduce both intra and interwarp conflicts and maximize data locality.
In contrast to their strong computing power, gpus have limited onchip memory space which is easy to be inadequate. The massive amount of memory requests generated by gpus cause cache contention and resource congestion. We observe that applications can generally be classified as either networksensitive or networkinsensitive. Gpu enenrgy efficiency through softwarehardware codesign. Proceedings of the 2014 international workshop on data intensive scalable computing systems, discs 14, new orleans, louisiana, usa, november 1621, 2014. First, we introduce a new cache indexing method that can adapt to memory accesses with different strides in this pattern, eliminate intrawarp associativity conflicts, and improves gpu cache performance. Cache memory software free download cache memory top 4 download offers free software downloads for windows, mac, ios and android computers and mobile devices. In proceedings of the ieee international symposium on performance analysis of systems and software ispass, new brunswick, nj ispass 2012 acceptance rate. The gpu scheduling mechanism swaps warps to hide memory latency. In one embodiment, a system implements hardware acceleration of cats to manage the dependencies and scheduling of an application composed of multiple tasks. When gpus perform memory accesses, they usually do so through caches, just like cpus do. We evaluate caching effectiveness of gpu data caches for both memory coherent and memory divergent gpgpu benchmarks, and present the problem of partial caching in existing gpu cache management. Complicate programming not always performance portable not guaranteed to improve performance sometimes impossible improve performance of programs with memory divergence. Divergenceaware warp scheduling uses the information gathered from warp 0 to predict that the data loaded by warp 1s active threads will evict data reused by warp 0 which is still.
Unlike prior work on cacheconscious wavefront scheduling, which makes. Even though the impacts of memory divergence can be alleviated through various software techniques, architectural support for memory divergence mitigation is still highly desirable to ease the complexity in the programming and optimization of gpuaccelerated dataintensive applications. Access patternaware cache management for improving data. Threads of a given threadblock can access the onchip programmermanaged cache, termed shared memory. See the complete profile on linkedin and discover tors connections and jobs at similar companies. Arunkumar 4 proposed an instruction and memory divergence cache management method, based on the studying reuse behavior and spatial utilization of cache lines using program level information. The pc skip table contains one entry for each pc currently being skipped.
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