2012 21st International Conference on Parallel Architectures and Compilation Techniques (PACT)最新文献

{"title":"Boost.SIMD: Generic programming for portable SIMDization","authors":"Pierre Estérie, J. Falcou, Mathias Gaunard, J. Lapresté","doi":"10.1145/2568058.2568063","DOIUrl":"https://doi.org/10.1145/2568058.2568063","url":null,"abstract":"SIMD extensions have been a feature of choice for processor manufacturers for a couple of decades. Designed to exploit data parallelism in applications at the instruction level and provide significant accelerations, these extensions still require a high level of expertise or the use of potentially fragile compiler support or vendor-specific libraries. In this poster, we present Boost.SIMD, a C++ template library that simplifies the exploitation of SIMD hardware within a standard C++ programming model.","PeriodicalId":403855,"journal":{"name":"2012 21st International Conference on Parallel Architectures and Compilation Techniques (PACT)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128319922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Many-thread aware instruction-level parallelism: Architecting shader cores for GPU computing","authors":"Ping Xiang, Yi Yang, Mike Mantor, Norman Rubin, Huiyang Zhou","doi":"10.1145/2370816.2370890","DOIUrl":"https://doi.org/10.1145/2370816.2370890","url":null,"abstract":"","PeriodicalId":403855,"journal":{"name":"2012 21st International Conference on Parallel Architectures and Compilation Techniques (PACT)","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123240574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Introducing Hierarchy-awareness in replacement and bypass algorithms for last-level caches","authors":"Mainak Chaudhuri, Jayesh Gaur, Nithiyanandan Bashyam, S. Subramoney, Joseph Nuzman","doi":"10.1145/2370816.2370860","DOIUrl":"https://doi.org/10.1145/2370816.2370860","url":null,"abstract":"The replacement policies for the last-level caches (LLCs) are usually designed based on the access information available locally at the LLC. These policies are inherently sub-optimal due to lack of information about the activities in the inner-levels of the hierarchy. This paper introduces cache hierarchy-aware replacement (CHAR) algorithms for inclusive LLCs (or L3 caches) and applies the same algorithms to implement efficient bypass techniques for exclusive LLCs in a three-level hierarchy. In a hierarchy with an inclusive LLC, these algorithms mine the L2 cache eviction stream and decide if a block evicted from the L2 cache should be made a victim candidate in the LLC based on the access pattern of the evicted block. Ours is the first proposal that explores the possibility of using a subset of L2 cache eviction hints to improve the replacement algorithms of an inclusive LLC. The CHAR algorithm classifies the blocks residing in the L2 cache based on their reuse patterns and dynamically estimates the reuse probability of each class of blocks to generate selective replacement hints to the LLC. Compared to the static re-reference interval prediction (SRRIP) policy, our proposal offers an average reduction of 10.9% in LLC misses and an average improvement of 3.8% in instructions retired per cycle (IPC) for twelve single-threaded applications. The corresponding reduction in LLC misses for one hundred 4-way multi-programmed workloads is 6.8% leading to an average improvement of 3.9% in through-put. Finally, our proposal achieves an 11.1% reduction in LLC misses and a 4.2% reduction in parallel execution cycles for six 8-way threaded shared memory applications compared to the SRRIP policy. In a cache hierarchy with an exclusive LLC, our CHAR proposal offers an effective algorithm for selecting the subset of blocks (clean or dirty) evicted from the L2 cache that need not be written to the LLC and can be bypassed. Compared to the TC-AGE policy (analogue of SRRIP for exclusive LLC), our best exclusive LLC proposal improves average throughput by 3.2% while saving an average of 66.6% of data transactions from the L2 cache to the on-die interconnect for one hundred 4-way multi-programmed workloads. Compared to an inclusive LLC design with an identical hierarchy, this corresponds to an average throughput improvement of 8.2% with only 17% more data write transactions originating from the L2 cache.","PeriodicalId":403855,"journal":{"name":"2012 21st International Conference on Parallel Architectures and Compilation Techniques (PACT)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114616927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Power-efficient time-sensitive mapping in heterogeneous systems","authors":"Cong Liu, Jian Li, Wei Huang, J. Rubio, W. Speight, Xiaozhu Lin","doi":"10.1145/2370816.2370822","DOIUrl":"https://doi.org/10.1145/2370816.2370822","url":null,"abstract":"Heterogeneous systems that contain multiple types of resources, such as CPUs and GPUs, are becoming increasingly popular thanks to the potential of achieving high performance and energy efficiency. In such systems, the problem of data mapping and communication for time-sensitive applications while reducing power and energy consumption is more challenging, since applications may have varied data management and computing patterns on different types of resources. In this paper, we propose power-aware maping techniques for CPU/GPU heterogeneous system that are able to meet applications' timing requirements while reducing power and energy consumption by applying DVFS on both CPUs and GPUs. We have implemented the proposed techniques in a real CPU/GPU heterogeneous system. Experimental results with several data analytics workloads show that compared to performance-driven mapping, our power-efficient mapping techniques can often achieve a reduction of more than 20% in power and energy consumption.","PeriodicalId":403855,"journal":{"name":"2012 21st International Conference on Parallel Architectures and Compilation Techniques (PACT)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114626749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"APCR: An adaptive physical channel regulator for On-Chip Interconnects","authors":"Lei Wang, Poornachandran Kumar, K. H. Yum, Eun Jung Kim","doi":"10.1145/2370816.2370830","DOIUrl":"https://doi.org/10.1145/2370816.2370830","url":null,"abstract":"Chip Multi-Processor (CMP) architectures have become mainstream for designing processors. With a large number of cores, Network-On-Chip (NOC) provides a scalable communication method for CMP architectures, where wires become abundant resources available inside the chip. NOC must be carefully designed to meet constraints of power and area, and provide ultra low latencies. In this paper, we propose an Adaptive Physical Channel Regulator (APCR) for NOC routers to exploit huge wiring resources. The flit size in an APCR router is less than the physical channel width (phit size) to provide finer granularity flow control. An APCR router allows flits from different packets or flows to share the same physical channel in a single cycle. The three regulation schemes (Monopolizing, Fair-sharing and Channel-stealing) intelligently allocate the output channel resources considering not only the availability of physical channels but the occupancy of input buffers. In an APCR router, each Virtual Channel can forward a dynamic number of flits every cycle depending on the run-time network status. Our simulation results using a detailed cycle-accurate simulator show that an APCR router improves the network throughput by over 100% in synthetic workloads, compared with a traditional design with the same buffer size. An APCR router can outperform a traditional router even if the buffer size is halved.","PeriodicalId":403855,"journal":{"name":"2012 21st International Conference on Parallel Architectures and Compilation Techniques (PACT)","volume":"15 12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127664792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Shared memory multiplexing: A novel way to improve GPGPU throughput","authors":"Yi Yang, Ping Xiang, Mike Mantor, Norman Rubin, Huiyang Zhou","doi":"10.1145/2370816.2370858","DOIUrl":"https://doi.org/10.1145/2370816.2370858","url":null,"abstract":"On-chip shared memory (a.k.a. local data share) is a critical resource to many GPGPU applications. In current GPUs, the shared memory is allocated when a thread block (also called a workgroup) is dispatched to a streaming multiprocessor (SM) and is released when the thread block is completed. As a result, the limited capacity of shared memory becomes a bottleneck for a GPU to host a high number of thread blocks, limiting the otherwise available thread-level parallelism (TLP). In this paper, we propose software and/or hardware approaches to multiplex the shared memory among multiple thread blocks. Our proposed approaches are based on our observation that the current shared memory management reserves shared memory too conservatively, for the entire lifetime of a thread block. If the shared memory is allocated only when it is actually used and freed immediately after, more thread blocks can be hosted in an SM without increasing the shared memory capacity. We propose three software approaches to enable shared memory multiplexing and implement them using a source-to-source compiler. The experimental results show that our proposed software approaches effectively improve the throughput of many GPGPU applications on both NVIDIA GTX285 and GTX480 GPUs (an average of 1.44X on GTX285, 1.70X on GTX480 with 16kB shared memory, and 1.26X on GTX480 with 48kB shared memory). We also propose hardware support for shared memory multiplexing, which incurs minor hardware changes to existing hardware and enables significant performance improvements (an average of 1.53X) to be achieved with very little change in GPGPU code.","PeriodicalId":403855,"journal":{"name":"2012 21st International Conference on Parallel Architectures and Compilation Techniques (PACT)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133137496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Workload and power budget partitioning for single-chip heterogeneous processors","authors":"Hao Wang, V. Sathish, Ripudaman Singh, M. Schulte, N. Kim","doi":"10.1145/2370816.2370873","DOIUrl":"https://doi.org/10.1145/2370816.2370873","url":null,"abstract":"With technology scaling, manufacturers are integrating both CPU and GPU cores in a single chip to improve the throughput of emerging applications. To maximize the throughput of a single-chip heterogeneous processor (SCHP), the chip power budget shared between the CPU and GPU must be effectively utilized. At the same time, the CPU and GPU in an SCHP must each satisfy its own power constraint. Furthermore, the power budget allocated to the CPU and GPU impacts performance. In this paper, using a detailed cycle-level SCHP simulator, we first demonstrate that the joint optimization of workload and power budget partitioning between the CPU and GPU can provide 13% higher throughput than the optimization of workload partitioning alone under a fixed power budget allocation to the CPU and GPU. Second, we propose an effective runtime algorithm that can determine near-optimal or optimal combinations of workload and power budget partitioning. The algorithm exploits the runtime power efficiencies of the workload executed on the CPU and the GPU. Using the detailed cycle-level SCHP simulator, we show that within five to eight kernel invocations the algorithm can achieve 96% of the maximum throughput obtained by an exhaustive search algorithm. Finally, we demonstrate comparable throughput improvements when we apply the algorithm to a commercial computing system with an SCHP.","PeriodicalId":403855,"journal":{"name":"2012 21st International Conference on Parallel Architectures and Compilation Techniques (PACT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115596491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Acceleration of bulk memory operations in a heterogeneous multicore architecture","authors":"Jong-Hyuk Lee, Ziyi Liu, Xiaonan Tian, Dong Hyuk Woo, W. Shi, Dainis Boumber, Yonghong Yan, Kyeong-An Kwon","doi":"10.1145/2370816.2370877","DOIUrl":"https://doi.org/10.1145/2370816.2370877","url":null,"abstract":"In this paper, we present a novel approach of using the integrated GPU to accelerate conventional operations that are normally performed by the CPUs, the bulk memory operations, such as memcpy or memset. Offloading the bulk memory operations to the GPU has many advantages, i) the throughput driven GPU outperforms the CPU on the bulk memory operations; ii) for on-die GPU with unified cache between the GPU and the CPU, the GPU private caches can be leveraged by the CPU for storing moved data and reducing the CPU cache bottleneck; iii) with additional lightweight hardware, asynchronous offload can be supported as well; and iv) different from the prior arts using dedicated hardware copy engines (e.g., DMA), our approach leverages the exiting GPU hardware resources as much as possible. The performance results based on our solution showed that offloaded bulk memory operations outperform CPU up to 4.3 times in micro benchmarks while still using less resources. Using eight real world applications and a cycle based full system simulation environment, the results showed 30% speedup for five, more than 20% speedup for two of the eight applications.","PeriodicalId":403855,"journal":{"name":"2012 21st International Conference on Parallel Architectures and Compilation Techniques (PACT)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115681227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Base-delta-immediate compression: Practical data compression for on-chip caches","authors":"Gennady Pekhimenko, V. Seshadri, O. Mutlu, Phillip B. Gibbons, M. Kozuch, T. Mowry","doi":"10.1145/2370816.2370870","DOIUrl":"https://doi.org/10.1145/2370816.2370870","url":null,"abstract":"Cache compression is a promising technique to increase on-chip cache capacity and to decrease on-chip and off-chip bandwidth usage. Unfortunately, directly applying well-known compression algorithms (usually implemented in software) leads to high hardware complexity and unacceptable decompression/compression latencies, which in turn can negatively affect performance. Hence, there is a need for a simple yet efficient compression technique that can effectively compress common in-cache data patterns, and has minimal effect on cache access latency.","PeriodicalId":403855,"journal":{"name":"2012 21st International Conference on Parallel Architectures and Compilation Techniques (PACT)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115926182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Complexity-effective multicore coherence","authors":"Alberto Ros, S. Kaxiras","doi":"10.1145/2370816.2370853","DOIUrl":"https://doi.org/10.1145/2370816.2370853","url":null,"abstract":"Much of the complexity and overhead (directory, state bits, invalidations) of a typical directory coherence implementation stems from the effort to make it “invisible” even to the strongest memory consistency model. In this paper, we show that a much simpler, directory-less/broadcast-less, multicore coherence can outperform a directory protocol but without its complexity and overhead. Motivated by recent efforts to simplify coherence, we propose a hardware approach that does not require any application guidance. The cornerstone of our approach is a dynamic, application-transparent, write-policy (write-back for private data, write-through for shared data), simplifying the protocol to just two stable states. Self-invalidation of the shared data at synchronization points allows us to remove the directory (and invalidations) completely, with just a data-race-free guarantee from software. This leads to our main result: a virtually costless coherence that outperforms a MESI directory protocol (by 4.8%) while at the same time reducing shared cache and network energy consumption (by 14.2%) for 15 parallel benchmarks, on 16 cores.","PeriodicalId":403855,"journal":{"name":"2012 21st International Conference on Parallel Architectures and Compilation Techniques (PACT)","volume":"64 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128300528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}