カーネル実行時のイベントは以下のものがあります。これらは同時に4つまでしか設定できません。
Energy expenses are becoming an increasingly important
fraction of data center operating costs. At the same time,
the energy expense per unit of computation can vary significantly
between two different locations. In this paper,
we characterize the variation due to fluctuating electricity
prices and argue that existing distributed systems should be
able to exploit this variation for significant economic gains.
Electricity prices exhibit both temporal and geographic variation,
due to regional demand differences, transmission inefficiencies,
and generation diversity. Starting with historical
electricity prices, for twenty nine locations in the US, and
network traffic data collected on Akamai’s CDN, we use simulation
to quantify the possible economic gains for a realistic
workload. Our results imply that existing systems may be
able to save millions of dollars a year in electricity costs, by
being cognizant of locational computation cost differences.
Topics of particular interest include, but are not limited to:
| Paper Submission | Extended to November 25, 2009 - 12:00 pm, Hawaii time (GMT -10) |
| Author Notification | February 8, 2010 |
| Final Manuscript Due | March 15, 2010 |
General purpose computing on graphics processing units (GPUs) introduces the challenge of scheduling task parallel workloads on devices designed for data parallel applications. This paper analyzes the problem, and presents a method for merging workloads such that they can be run concurrently on an NVIDIA GPU. Some kernels do not fully utilize the processing power of the GPU, and thus overall throughput will increase when running two kernels alongside one another. Our approach scans a queue of independent CUDA kernels (i.e., code segments that will run on the GPU), across processes or from within the same process, and evaluates whether merging the kernels would increase both throughput on the device and overall efficiency of the computing platform. Using kernels from microbenchmarks and a Nearest Neighbor application we show that throughput is increased in all cases where the GPU would have been underused by a single kernel. An exception is the case of memory-bound kernels, seen in a Nearest Neighbor application, for which the execution time still outperforms the same kernels executed serially by 12-20%. It can also be benefitial to choose a merged kernel that over-extends the GPU resources, as we show the worst case to be bounded by executing the kernels serially. This paper also provides an analysis of the latency penalty that can occur when two kernels with varying completion times are merged
Submission Deadline: December 14, 2009
Notification of Acceptance: January 15, 2010
Camera-Ready Copy Due: February 1, 2010
Workshop: April 23, 2010
typedef enum
{
culaNoError, // No error
culaNotInitialized, // CULA has not been initialized
culaNoHardware, // No hardware is available to run
culaFeatureNotImplemented, // The requested feature has not been implemented
culaInsufficientComputeCapability, // Available GPUs do not support the requested operation
culaInsufficientMemory, // There is insufficient memory to continue
culaArgumentError, // An invalid argument was passed to a function
culaDataError, // An operation could not complete because of singular data
culaBlasError, // A blas error was encountered
culaRuntimeError // A runtime error has occurred
}culaStatus;
と status が見えるので、NoError 以外のときはこれをデバッグで
まずは表示すべき
ソケットダイレクトプロトコルSDP は，伝統的なソケットストリームAPIを提供しつつ，RDMA プロトコルに直接マッピングすることにより高性能な通信を可能にするプロトコルである．この論文では、Infiniband 上に実装された SDP の通信性能をクラスタ上で評価し、ギガビットネットイーサネットと IPoIB とを比較している。結論としては、SDP は小さなメッセージサイズに対しては優位性がないが、大きなメッセージサイズに対してはレイテンシおよびスループットで有利という。ストリーム処理における高速データ転送に参考になる論文だ。
特異値分解の C 実装は以下にあります。
http://www.netlib.org/svdpack/
file svdpackc.tgz
for C version of SVDPACK for computing the SVD of large
, sparse real matrices
by Michael W. Berry et al.
ref M. Berry et al., "SVDPACKC (Version 1.0) User's
, Guide", Univ. of Tennessee Tech. Report CS-93-194,
, April 1993 (Revised October 1996)
Linear algebra algorithms are fundamental to many computing applications. Modern GPUs are suited for many general purpose processing tasks and have emerged as inexpensive high performance co-processors due to their tremendous computing power. In this paper, we present the implementation of singular value decomposition (SVD) of a dense matrix on GPU using the CUDA programming model. SVD is implemented using the twin steps of bidiagonalization followed by diagonalization. It has not been implemented on the GPU before. Bidiagonalization is implemented using a series of Householder transformations which map well to BLAS operations. Diagonalization is performed by applying the implicitly shifted QR algorithm. Our complete SVD implementation outperforms the MATLAB and Intel ®Math Kernel Library (MKL) LAPACK implementation significantly on the CPU. We show a speedup of upto 60 over the MATLAB implementation and upto 8 over the Intel MKL implementation on a Intel Dual Core 2.66GHz PC on NVIDIA GTX 280 for large matrices. We also give results for very large matrices on NVIDIA Tesla S1070.
データベースシステム研究会は、主に、DBMS (Database Management System) 技術、データモデリング、情報検索、ハイパーテキスト・ハイパーメディア、マルチメディアデータベース、データベース高度応用などの分野を対象として研究 会活動を行ってきております。データベース系の他の団体との共催イベントを毎年 3 回程度（7月，11月，3月）開催しています。
インターネット・イントラネットとマルチメディア技術の進展により、データベースシステム技術の重要性と関心が一層大きくなるとともに、ネットワー ク・マルチメディア時代の新しい情報共有の基盤ソフトウェアとしての新しいデータベースシステム像が求められています。実際、これに関する研究活動が活発 化しており、研究会としても積極的にこの分野を取り上げていきたいと考えています。データベース技術の需要は増える方向にあり、今後も学会活動に積極的に 取り組んでいきたいと考えております。
