314 research outputs found
ΠΡΠΎΡΠ΅ΡΡΠΎΡΡ Π. Π. ΠΠΎΡΠΈΠΊΠΎΠ²Ρ - 70 Π»Π΅Ρ
1 ΡΠ½Π²Π°ΡΡ 2012 Π³. ΠΎΡΠΌΠ΅ΡΠΈΠ» ΡΠ±ΠΈΠ»Π΅ΠΉ Π΄ΠΎΠΊΡΠΎΡ ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΈΡ
Π½Π°ΡΠΊ, ΠΏΡΠΎΡΠ΅ΡΡΠΎΡ, ΠΠ°ΡΠ»ΡΠΆΠ΅Π½Π½ΡΠΉ Π΄Π΅ΡΡΠ΅Π»Ρ Π½Π°ΡΠΊΠΈ Π Π€, ΠΠΎΡΠ΅ΡΠ½ΡΠΉ ΡΠ°Π±ΠΎΡΠ½ΠΈΠΊ Π²ΡΡΡΠ΅Π³ΠΎ ΠΏΡΠΎΡΠ΅ΡΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ Π Π€ ΠΈ ΠΠΎΡΠ΅ΡΠ½ΡΠΉ ΡΠ°Π±ΠΎΡΠ½ΠΈΠΊ Π½Π°ΡΠΊΠΈ ΠΈ ΡΠ΅Ρ
Π½ΠΈΠΊΠΈ Π Π€ ΠΠ½Π°ΡΠΎΠ»ΠΈΠΉ ΠΠΈΡ
Π°ΠΉΠ»ΠΎΠ²ΠΈΡ ΠΠΎΡΠΈΠΊΠΎΠ²
ΠΠΈΠ±ΡΠΎΡΡΠ°Π½ΡΠΏΠΎΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ Π΄Π΅ΡΠ°Π»Π΅ΠΉ: ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°
MPI is the predominant model for parallel programming in technical high performance computing. With an increasing number of cores and threads in cluster nodes the question arises whether pure MPI is an appropriate approach to utilize todayοΏ½s compute clusters or if it is profitable to add another layer of parallelism within the nodes by applying OpenMP on a lower level. Investing a limited amount of manpower, we add OpenMP directives to three MPI production codes and compare and analyze the performance varying the number of MPI processes per node and the number of OpenMP threads per MPI process on current CMP/CMT architectures
Π Π½Π΅ΠΊΠΎΡΠΎΡΡΡ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡΡ Π΄Π°Π»ΡΠ½Π΅ΠΉΡΠ΅ΠΉ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·Π°ΡΠΈΠΈ Π±Π΅ΡΠ°ΡΡΠΎΠ½ΠΎΠ²
The novel ScaleMP vSMP architecture employs commodity x86-based servers with an InfiniBand network to assemble a large shared memory system at an attractive price point. We examine this combined hardware- and softwareapproach of a DSM system using both system-level kernel benchmarks as well as real-world application codes. We compare this architecture with traditional shared memory machines and elaborate on strategies to tune application codes parallelized with OpenMP on multiple levels. Finally we summarize the necessary conditions which a scalable application has to fulfill in order to profit from the full potential of the ScaleMP approach
Π ΡΠ΅ΠΆΠΈΠΌΠ΅ ΠΊΡΠ°ΡΠΊΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ ΠΎΡΠΏΡΡΠΊΠΎΠ² Π±ΡΡΡΡΠΎΡΠ΅ΠΆΡΡΠ΅ΠΉ ΡΡΠ°Π»ΠΈ
ΠΠ»ΠΈΡΠ½ΠΈΠ΅ ΡΠΎΠ»ΡΠΈΠ½Ρ ΠΈ Π²Π·Π°ΠΈΠΌΠ½ΠΎΠ³ΠΎ ΡΠ°ΡΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΡ ΡΠ»ΠΎΠ΅Π² Π² ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡΠΈΠΈ ΠΆΠ΅Π»Π΅Π·ΠΎ-ΠΏΠΎΠ»ΠΈΡΡΠΈΠ»Π΅Π½ Π½Π° ΡΠ½Π΅ΡΠ³Π΅ΡΠΈΡΠ΅ΡΠΊΠΎΠ΅ ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠ΅ Π±ΡΡΡΡΡΡ Π½Π΅ΠΉΡΡΠΎΠ½ΠΎΠ²
Π Π°ΡΡΠ΅Ρ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΠ»ΡΡ Π΄Π»Ρ Π΄Π²ΡΡ
ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡΠΈΠΉ: ΠΆΠ΅Π»Π΅Π·ΠΎ-ΠΏΠΎΠ»ΠΈΡΡΠΈΠ»Π΅Π½, ΠΏΠΎΠ»ΠΈΡΡΠΈΠ»Π΅Π½-ΠΆΠ΅Π»Π΅Π·ΠΎ. Π£ΡΠ°Π²Π½Π΅Π½ΠΈΠ΅ ΠΏΠ΅ΡΠ΅Π½ΠΎΡΠ° Π½Π΅ΠΉΡΡΠΎΠ½ΠΎΠ² ΡΠ΅ΡΠ°Π»ΠΎΡΡ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ ΠΠΎΠ½ΡΠ΅-ΠΠ°ΡΠ»ΠΎ Π² ΡΡΠ°Π½ΡΠΏΠΎΡΡΠ½ΠΎΠΌ ΠΏΡΠΈΠ±Π»ΠΈΠΆΠ΅Π½ΠΈΠΈ. ΠΠΎΠ»ΡΡΠ΅Π½Π½ΡΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΡΠ²ΠΈΠ΄Π΅ΡΠ΅Π»ΡΡΡΠ²ΡΡΡ ΠΎ Π·Π½Π°ΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠΌ Π²Π»ΠΈΡΠ½ΠΈΠΈ ΡΠ»ΠΎΡ Π»Π΅Π³ΠΊΠΎΠ³ΠΎ ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»Π° Π½Π° Π²Π΅Π»ΠΈΡΠΈΠ½Ρ ΠΏΠΎΡΠΎΠΊΠ° ΠΈ ΡΠ½Π΅ΡΠ³Π΅ΡΠΈΡΠ΅ΡΠΊΠΎΠ΅ ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠ΅ ΠΏΡΠΎΡΠ΅Π΄ΡΠΈΡ
Π½Π΅ΠΉΡΡΠΎΠ½ΠΎΠ², ΡΡΠΎ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΠΎΡΡΡΠ΅ΡΡΠ²ΠΈΡΡ Π²ΡΠ±ΠΎΡ ΡΠ½Π΅ΡΠ³Π΅ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π΄ΠΈΠ°ΠΏΠ°Π·ΠΎΠ½Π° ΡΠ΅Π³ΠΈΡΡΡΠ°ΡΠΈΠΈ ΠΏΡΠΎΡΠ΅Π΄ΡΠ΅Π³ΠΎ ΠΏΠΎΡΠΎΠΊΠ° Π΄Π»Ρ ΠΏΠΎΠ»ΡΡΠ΅Π½ΠΈΡ ΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠΉ ΠΈΠ·Π±ΠΈΡΠ°ΡΠ΅Π»ΡΠ½ΠΎΠΉ ΡΡΠ²ΡΡΠ²ΠΈΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ ΠΊ ΡΠ»ΠΎΡ Π»Π΅Π³ΠΊΠΎΠ³ΠΎ ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»Π°
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