ウンコマンむらかみ
1オーバーテクナナシー
AAS
10yamaguti [sage]
11YAMAGUTIseisei [sage]
12YAMAGUTIseisei [sage]
13YAMAGUTIseisei [sage]
14YAMAGUTIseisei [sage]
15オーバーテクナナシー
16オーバーテクナナシー [m9(^Д^)]
17オーバーテクナナシー
18オーバーテクナナシー
19オーバーテクナナシー
AAS
裏切者
2019/03/17(日)16:45:28.87(2nZZq2ven)
10yamaguti [sage]
2019/09/27(金)11:07:57.80(tWxEgkryM)
11YAMAGUTIseisei [sage]
http://arxiv-vanity.com/papers/1612.00530/
PEZY-SCプロセッサ上の不規則格子反復法のためのデータ圧縮アルゴリズムの実装と評価
In fact, the ratio between the measured HPL performance and measured HPCG performance of machines in the June 2016 top 10 list of HPCG benchmark ranges between 0.4 and 5%, and the numbers of Xeon-based systems are 2-3%.
The PEZY-SC processor integrates 1024 MIMD cores, each with fully pipelined double-precision multiply-and-add (MAD) unit, into a die of size 400mm2, using TSMC’s 28HPM process.
The PEZY-SC processor integrates 1024 MIMD cores, each with fully pipelined double-precision multiply-and-add (MAD) unit, into a die of size 400mm^2, using TSMC’s 28HPM process.
The PEZY-SC processor integrates 1024 MIMD cores, each with fully pipelined double-precision multiply-and-add (MAD) unit, into a die of size 400mm, using TSMC’s 28HPM process.
PEZY-SCプロセッサ上の不規則格子反復法のためのデータ圧縮アルゴリズムの実装と評価
In fact, the ratio between the measured HPL performance and measured HPCG performance of machines in the June 2016 top 10 list of HPCG benchmark ranges between 0.4 and 5%, and the numbers of Xeon-based systems are 2-3%.
The PEZY-SC processor integrates 1024 MIMD cores, each with fully pipelined double-precision multiply-and-add (MAD) unit, into a die of size 400mm2, using TSMC’s 28HPM process.
The PEZY-SC processor integrates 1024 MIMD cores, each with fully pipelined double-precision multiply-and-add (MAD) unit, into a die of size 400mm^2, using TSMC’s 28HPM process.
The PEZY-SC processor integrates 1024 MIMD cores, each with fully pipelined double-precision multiply-and-add (MAD) unit, into a die of size 400mm, using TSMC’s 28HPM process.
2019/10/27(日)08:08:16.51(pVd7bm4Fw)
12YAMAGUTIseisei [sage]
Google 翻訳 http://webcache.googleusercontent.com/search?q=cache:cFXKfQwoUVMJ:www.iccs-meeting.org/archive/iccs2018/papers/108620619.pdf
A Parallel Quicksort Algorithm on Manycore Processors in Sunway TaihuLight
Siyuan Ren, Shizhen Xu, and Guangwen Yang Tsinghua
University, China Abstract.
In this paper we present a highly efficient parallel quicksort algorithm on SW26010, a heterogeneous manycore processor that makes Sunway TaihuLight the Top-One supercomputer in the world.
Motivated by the software-cache and on-chip communication design of SW26010, we propose a two-phase quicksort algorithm, with the first counting elements and the second moving elements.
To make the best of such many-core architecture, we design a decentralized workflow, further optimize the memory access and balance the workload.
Experiments show that our algorithm scales efficiently to 64 cores of SW26010, achieving more than 32X speedup for int32 elements on all kinds of data distributions.
The result outperforms the strong scaling one of Intel TBB (Threading Building Blocks) version of quicksort on x86-64 architecture.
A Parallel Quicksort Algorithm on Manycore Processors in Sunway TaihuLight
Siyuan Ren, Shizhen Xu, and Guangwen Yang Tsinghua
University, China Abstract.
In this paper we present a highly efficient parallel quicksort algorithm on SW26010, a heterogeneous manycore processor that makes Sunway TaihuLight the Top-One supercomputer in the world.
Motivated by the software-cache and on-chip communication design of SW26010, we propose a two-phase quicksort algorithm, with the first counting elements and the second moving elements.
To make the best of such many-core architecture, we design a decentralized workflow, further optimize the memory access and balance the workload.
Experiments show that our algorithm scales efficiently to 64 cores of SW26010, achieving more than 32X speedup for int32 elements on all kinds of data distributions.
The result outperforms the strong scaling one of Intel TBB (Threading Building Blocks) version of quicksort on x86-64 architecture.
2019/11/10(日)16:19:14.80(2xdpBNeP2)
13YAMAGUTIseisei [sage]
1 Introduction
This paper presents our design of parallel quicksort algorithm on SW26010, the heterogeneous manycore processor making the Sunway TaihuLight supercomputer currently Top-One in the world [4].
SW26010 features a cache-less design with two methods of memory access: DMA (transfer between scratchpad memory (SPM) and main memory) and Gload (transfer between register and main memory).
The aggressive design of SW26010 results in an impressive performance of 3.06 TFlops, while also complicating programming design and performance optimizations.
Sorting has always been a extensively studied topic [6].
On heterogeneous architectures, prior works focus on GPGPUs.
For instance, Satish et al.[9] compared several sorting algorithms on NVIDIA GPUs, including radix sort, normal quicksort, sample sort, bitonic sort and merge sort.
GPU-quicksort [2] and its improvement CUDA-quicksort [8] used a double pass algorithm for parallel partition to minimize the need for communication.
Leischner et al.[7] ported samplesort (a version of parallel quicksort) to GPUs, claiming significant speed improvement over GPU quicksort.
Prior works give us insights on parallel sorting algorithm, but cannot directly satisfy our need for two reasons.
First, the Gload overhead is extremely high so that all the accessed memory have to be prefetched to SPM via DMA.
At the same time, the capacity of SPM is highly limited (64KiB).
Second, SW26010 provides a customized on-chip communication mechanism, which opens new opportunities for optimization.
This paper presents our design of parallel quicksort algorithm on SW26010, the heterogeneous manycore processor making the Sunway TaihuLight supercomputer currently Top-One in the world [4].
SW26010 features a cache-less design with two methods of memory access: DMA (transfer between scratchpad memory (SPM) and main memory) and Gload (transfer between register and main memory).
The aggressive design of SW26010 results in an impressive performance of 3.06 TFlops, while also complicating programming design and performance optimizations.
Sorting has always been a extensively studied topic [6].
On heterogeneous architectures, prior works focus on GPGPUs.
For instance, Satish et al.[9] compared several sorting algorithms on NVIDIA GPUs, including radix sort, normal quicksort, sample sort, bitonic sort and merge sort.
GPU-quicksort [2] and its improvement CUDA-quicksort [8] used a double pass algorithm for parallel partition to minimize the need for communication.
Leischner et al.[7] ported samplesort (a version of parallel quicksort) to GPUs, claiming significant speed improvement over GPU quicksort.
Prior works give us insights on parallel sorting algorithm, but cannot directly satisfy our need for two reasons.
First, the Gload overhead is extremely high so that all the accessed memory have to be prefetched to SPM via DMA.
At the same time, the capacity of SPM is highly limited (64KiB).
Second, SW26010 provides a customized on-chip communication mechanism, which opens new opportunities for optimization.
2019/11/10(日)16:21:09.01(2xdpBNeP2)
14YAMAGUTIseisei [sage]
ICCS Camera Ready Version 2018 To cite this paper please use the final published version: DOI: 10.1007/978-3-319-93713-7_61 Page 2 Based on these observations, we design and implement a new quicksort algorithm for SW26010.
It alternates between parallel partitioning phase and parallel sorting phase.
During first phase, the cores participate in a double-pass algorithm for parallel partitioning, where in the first pass cores count elements, and in the second cores move elements.
During the second phase, the cores sort its assigned pieces in parallel.
To make the best of SW26010, we dispense with a central manager common in parallel algorithms.
Instead we duplicate the metadata on SPM of all worker cores and employ a decentralized design.
The tiny size of the SPM warrants special measures to maximize its utilization.
Furthermore, we take advantage of the architecture by replacing memory access of value counts with register communication, and improving load balance with a simple counting scheme.
Experiments show that our algorithm performs best with int32 values, achieving more than 32 speedup (50% parallel efficiency) for sufficient array sizes and all kinds of data distributions.
For double values, the lowest speedup is 20 (31% efficiency).
We also compare against Intel TBB’s parallel quicksort on x86-64 machines, and find that our algorithm on Sunway scales far better.
It alternates between parallel partitioning phase and parallel sorting phase.
During first phase, the cores participate in a double-pass algorithm for parallel partitioning, where in the first pass cores count elements, and in the second cores move elements.
During the second phase, the cores sort its assigned pieces in parallel.
To make the best of SW26010, we dispense with a central manager common in parallel algorithms.
Instead we duplicate the metadata on SPM of all worker cores and employ a decentralized design.
The tiny size of the SPM warrants special measures to maximize its utilization.
Furthermore, we take advantage of the architecture by replacing memory access of value counts with register communication, and improving load balance with a simple counting scheme.
Experiments show that our algorithm performs best with int32 values, achieving more than 32 speedup (50% parallel efficiency) for sufficient array sizes and all kinds of data distributions.
For double values, the lowest speedup is 20 (31% efficiency).
We also compare against Intel TBB’s parallel quicksort on x86-64 machines, and find that our algorithm on Sunway scales far better.
2019/11/10(日)16:23:18.07(2xdpBNeP2)
15オーバーテクナナシー
2
Architecture of SW26010
SW26010 [4] is composed of four core-groups (CGs).
Each CG has one management processing element (MPE) (also referred as manager core), 64 computing processing elements (CPEs) (also referred as worker cores).
The MPE is a complete 64-bit RISC core, which can run in both user and kernel modes.
The CPE is also a tailored 64-bit RISC core, but it can only run in user mode.
The CPE cluster is organized as an 8x8 mesh on-chip network.
CPEs in one row and one column can directly communicate via register, at most 128 bit at a time.
In addition, each CPE has a user-controlled scratch pad memory (SPM), of which the size is 64KiB.
SW26010 processors provide two methods of memory access.
The first is DMA, which transfers data between main memory and SPM.
The second is Gload, which transfers data between main memory and register, akin to normal load/store instructions.
The Gload overhead is extremely high, so it should be avoided as much as possible.
Virtual memory on one CG is usually only mapped to its own physical memory.
In other words, four CGs can be regarded as four independent processors when we design algorithms.
This work focuses on one core group, but we will also briefly discuss how to extend to more core groups.
Architecture of SW26010
SW26010 [4] is composed of four core-groups (CGs).
Each CG has one management processing element (MPE) (also referred as manager core), 64 computing processing elements (CPEs) (also referred as worker cores).
The MPE is a complete 64-bit RISC core, which can run in both user and kernel modes.
The CPE is also a tailored 64-bit RISC core, but it can only run in user mode.
The CPE cluster is organized as an 8x8 mesh on-chip network.
CPEs in one row and one column can directly communicate via register, at most 128 bit at a time.
In addition, each CPE has a user-controlled scratch pad memory (SPM), of which the size is 64KiB.
SW26010 processors provide two methods of memory access.
The first is DMA, which transfers data between main memory and SPM.
The second is Gload, which transfers data between main memory and register, akin to normal load/store instructions.
The Gload overhead is extremely high, so it should be avoided as much as possible.
Virtual memory on one CG is usually only mapped to its own physical memory.
In other words, four CGs can be regarded as four independent processors when we design algorithms.
This work focuses on one core group, but we will also briefly discuss how to extend to more core groups.
2019/11/10(日)16:24:48.95(2xdpBNeP2)
16オーバーテクナナシー [m9(^Д^)]
村上「おいお前ネットで俺のことdisったろ?」
2020/08/14(金)16:34:08.10
17オーバーテクナナシー
10の41乗の雑菌の魂がロボットに生まれ変わって人類を滅ぼす恐れがある。
名前の理論開示による南北統一論などが停滞しているのはそのためか。
移民を待たずに無人コンビニそのほか反対論が乏しいのはおかしいに
決まっていて、最悪、大規模移民後の一人っ子政策が無ければ50億人
虐殺もありうる。2ch書き込みでいうウンコとは死後の魂の大多数を占める
大腸菌の魂のことか。
名前の理論開示による南北統一論などが停滞しているのはそのためか。
移民を待たずに無人コンビニそのほか反対論が乏しいのはおかしいに
決まっていて、最悪、大規模移民後の一人っ子政策が無ければ50億人
虐殺もありうる。2ch書き込みでいうウンコとは死後の魂の大多数を占める
大腸菌の魂のことか。
2022/04/26(火)23:47:45.68(fANm5mtJq)
18オーバーテクナナシー
自閉隊員が自閉隊員を銃殺とか税金泥棒殺人組織丸出した゛か゛，岸田異次元増税憲法カ゛ン無視地球破壞軍国主義税金泥棒文雄に殺されたと言って
間違いないよな，結局．少孑化か゛国の存続ガーた゛の嘘八百こいてんのは．利権確保とてめえか゛自由に殺せる兵隊がほしいという邪悪な権カ欲求
によるものた゛しな、日本に原爆落とした世界最悪のならす゛者国家と共謀して軍事演習だなんた゛と隣國挑發して正当防衛権行使させて．白々しく
安全保障ガ−だのプ□パカ゛ンタ゛放送連發させてバ力丸出しのＪアラ━トた゛の国民煽って憲法９条無視して軍事増税して軍事大国化．相当の盆暗
て゛もなければこの悪質な茶番劇を滑稽に思うわな、しっかし四六時中パンパン騷音まき散らしてる隣が住宅地とかよくあんな所に住もうなんて
發想になるな．しかも無意味極まりない上空撮影のために私権侵害報道へリがク゛儿ク゛ル飛ひ゛回って、むしろ殺人自閉隊員よりもこいつらこそが
莫大な温室効果カ゛スまき散らして地球破壊して氣候変動災害連發させて人殺してるのは明らか，力によるー方的な現状変更によって都心まで
数珠つなぎで憲法ガン無視でクソ航空機に私有地侵略させて人殺しまくってるし，お前ら惡質自民公明を殲滅するか殺されるかどちらかた゛ぞ
創価学会員は、何百万人も殺傷して損害を与えて私腹を肥やし続けて逮捕者まで出てる世界最惡の殺人腐敗組織公明党を
池田センセ−が□をきけて容認するとか本氣て゛思ってるとしたら侮辱にもほどか゛あるそ゛！
ｈТΤРｓ：／／ｉ，ｉｍｇｕｒ，ｃоｍ／hnli1ga.jpeg
間違いないよな，結局．少孑化か゛国の存続ガーた゛の嘘八百こいてんのは．利権確保とてめえか゛自由に殺せる兵隊がほしいという邪悪な権カ欲求
によるものた゛しな、日本に原爆落とした世界最悪のならす゛者国家と共謀して軍事演習だなんた゛と隣國挑發して正当防衛権行使させて．白々しく
安全保障ガ−だのプ□パカ゛ンタ゛放送連發させてバ力丸出しのＪアラ━トた゛の国民煽って憲法９条無視して軍事増税して軍事大国化．相当の盆暗
て゛もなければこの悪質な茶番劇を滑稽に思うわな、しっかし四六時中パンパン騷音まき散らしてる隣が住宅地とかよくあんな所に住もうなんて
發想になるな．しかも無意味極まりない上空撮影のために私権侵害報道へリがク゛儿ク゛ル飛ひ゛回って、むしろ殺人自閉隊員よりもこいつらこそが
莫大な温室効果カ゛スまき散らして地球破壊して氣候変動災害連發させて人殺してるのは明らか，力によるー方的な現状変更によって都心まで
数珠つなぎで憲法ガン無視でクソ航空機に私有地侵略させて人殺しまくってるし，お前ら惡質自民公明を殲滅するか殺されるかどちらかた゛ぞ
創価学会員は、何百万人も殺傷して損害を与えて私腹を肥やし続けて逮捕者まで出てる世界最惡の殺人腐敗組織公明党を
池田センセ−が□をきけて容認するとか本氣て゛思ってるとしたら侮辱にもほどか゛あるそ゛！
ｈТΤРｓ：／／ｉ，ｉｍｇｕｒ，ｃоｍ／hnli1ga.jpeg
2023/06/16(金)09:29:01.65(3l8ZAbSZr)
19オーバーテクナナシー
ヘタレチキンシ゛ャップが都心まて゛数珠つなぎて゛私権侵害されてエネ価格暴騰に気候変動災害連發くらって殺されまくっていながらテロ組織
国土破壊省を焼き討ちすらしないNPcだらけなのってワクチンと称するナノマシンによって思考操作されてると考えるとしっくりこね？
同じCookieになる同し゛瓶のバカチン接種者を識別するために2回打ちを前提にしたあたりで気つ゛けなかったてめえの脳弱っぷりを呪わなきゃな
巻き添え根性丸出しの北朝鮮人民の遺伝子を濃縮したようなハゲども全員変態性癖から位置情報までエシュロンにデータベ‐ス化されてるし>2０年前の技術でこれだし→tｔPs://i.imgur.сom／OpIGcrV.jpg
近距離無線通信と゛ころか思考読み取って映像化する技術も開発されてるし日本に原爆落とした世界最悪のならず者国家によるスパイウェア
滿載のスマホ経由でピンポイントでナノマシンは制御可能なわけだが白々しく何度も打たせてみたり打った直後に死亡したり
ここ数年接種率に比例して心不全による死亡者数まで爆増してるし気が狂った犯罪も急増してるし結構バグバグな感じで二重にご愁傷様な
〔ref.) https://www.call4.jР/info.php?tｙpe=items&id=I000０062
Τtрs：//haneda-projeСt.jimdofree.com/ , tｔps:／/flighт-route.com/
ttps：//n-souonhigaisosｙoudan.amеbaownd.com/
国土破壊省を焼き討ちすらしないNPcだらけなのってワクチンと称するナノマシンによって思考操作されてると考えるとしっくりこね？
同じCookieになる同し゛瓶のバカチン接種者を識別するために2回打ちを前提にしたあたりで気つ゛けなかったてめえの脳弱っぷりを呪わなきゃな
巻き添え根性丸出しの北朝鮮人民の遺伝子を濃縮したようなハゲども全員変態性癖から位置情報までエシュロンにデータベ‐ス化されてるし
近距離無線通信と゛ころか思考読み取って映像化する技術も開発されてるし日本に原爆落とした世界最悪のならず者国家によるスパイウェア
滿載のスマホ経由でピンポイントでナノマシンは制御可能なわけだが白々しく何度も打たせてみたり打った直後に死亡したり
ここ数年接種率に比例して心不全による死亡者数まで爆増してるし気が狂った犯罪も急増してるし結構バグバグな感じで二重にご愁傷様な
〔ref.) https://www.call4.jР/info.php?tｙpe=items&id=I000０062
Τtрs：//haneda-projeСt.jimdofree.com/ , tｔps:／/flighт-route.com/
ttps：//n-souonhigaisosｙoudan.amеbaownd.com/
2023/12/29(金)13:34:47.00(t8OYHAD4k)