ゴッゴルの岑は須賀原の勘原の恵律子です。

海軍学校を卒業後、ポルトガル海軍士官として奉職 一方で、それに反対する声もある このため、日テレ金9ドラマは、ネット再開後は16:30??17:25に繰り上げ、さらに1983年4月には沖縄テレビに移行した また、ドラッグや暴力沙汰などのロックではおなじみのスキャンダルとともに、彼らの発言もまたスキャンダラスなものが多く、注目を集めている 地理範囲はインドシナ半島、マレー半島、フィリピン諸島、マレー諸島、大スンダ列島、小スンダ列島、マルク（モルッカ）諸島、インドネシア領ニューギニア島 途中バリー・フォージはイギリス海軍コロッサス級トライアンフと合流し北朝鮮近海に進出、空軍機展開までの間戦線を持ちこたえた 3年に渡る困難な共同作業のすえ、1899年10月、作品が完成した 各国も競って多砲塔戦車を開発、試作したが、その頃起きた世界恐慌で、軍事費が大幅に削減されてしまい、単一砲塔の戦車と比べ、車体も大きく構造が複雑なため生産コストが高く、各国は多砲塔戦車の開発、生産の全てを停止、破棄してしまった シェイクスピアの「じゃじゃ馬馴らし」をベースにした「恋のからさわぎ」で男嫌いのヒロインを好演して注目される 『春秋』は、「年・四季・月・日 - 記事」という体裁をとっている 化学データベースは他の汎用データベースと異なり、部分構造検索を提供する点が特徴に挙げられる また、キーボードにNFER（無変換）キーが追加された 「生きるべきか死ぬべきかそれが問題だ」「ブルータス、お前もか」（"Et tu, Brute!" とラテン語で書かれた）など名台詞として人口に広く膾炙しているものもある 一般に知られているのが2号機 "ハーピュレイ" であり、試作機はΖガンダムよりも早く完成したものの、ティターンズでは当時ガンダリウムβの技術が限界に達していたためフレームの耐久性や装甲に難があり、最終的に開発は中止された 実際、選択公理を認めてしまうと一見直観に反する性質すらも証明できてしまう(バナッハ・タルスキの逆理) また、地下水で涵養されているため、集水域の開発はその地下水位を変化させ、周囲を開発しただけでも変質や減少、さらには消失してしまうことがある パンは当初、大麦から作られることが多かったが、小麦で焼いたもののほうが美味であることが知られるようになり、しだいに小麦でつくられることのほうが多くなった //アメリカ陸軍の航空部門は、第二次世界大戦が始まる5年前の1934年5月に超長距離大型爆撃機開発計画「プロジェクトA」を発足させた 録音放送が基本だが、不定期で生放送になる場合がある 堂守はカヴァラドッシのアリアの間、絵のモデルが礼拝に来る夫人であることに気づき呆れて、「ふざけるなら俗人にして、聖人は敬ってくれよ」と合いの手で歌い、画家に促されて退場する ギ酸というとアリを思い浮かべる人が多いが、すべてのアリがギ酸を持つわけではない 開催国を決定する投票日前日の定例理事会前のパーティー会場で、アフリカ理事らとの歓談から、趨勢を悟ったアベランジェ会長は、定例理事会で日韓両国による共同開催案を自ら提案、満場一致の拍手の賛成決議で定例理事会は幕を閉じた こちらもご覧下さい1997年は、水曜日から始まる平年である 5Vのみ対応の本体に3.3Vカードを挿すと危険なので、コネクタ部側面に誤挿入防止キーが設けられている 以前、ルイビル大学によって所有及び運営されていた、ここは現在スピンオフされ 公共ラジオ放送共同事業 によって運営されている 愛称は「あみ??ゴ」 A great advantage of the stream programming model lies in the kernel defining independent and local data usage.Kernel operations define the basic data unit, both as input and output. This allows the hardware to better allocate resources and schedule global I/O. Although usually not exposed in the programming model, the I/O operations seems to be much more advanced on stream processors (at least, on GPUs). I/O operations are also usually pipelined by themselves while chip structure can help hide latencies. Definition of the data unit is usually explicit in the kernel, which is expected to have well-defined inputs (possibly using structures, which is encouraged) and outputs. In some environments, output values are fixed (in GPUs for example, there is a fixed set of output attributes, unless this is relaxed). Having each computing block clearly independent and defined allows to schedule bulk read or write operations, greatly increasing cache and memory bus efficiency.Data locality is also explicit in the kernel. This concept is usually referred as kernel locality, identifying all the values which are short-lived to a single kernel invocation. All the temporaries are simply assumed to be local to each kernel invocation so, hardware or software can easily allocate them on fast registers. This is strictly related to degree of parallelism that can be exploited.Inside each kernel, producer-consumer relationships can be individuated by usual means while, when kernels are chained one after the another, this relationship is given by the model. This allows easier scheduling decisions because it's clear that if kernel B requires output from kernel A, it's obvious that A must be completed before B can be run (at least on the data unit being used). The Imagine chip's on-board stream controller module manages kernel loads and execution in hardware at runtime keeping a scoreboard of kernel dependencies (as told by the compiler) and can allow out-of-order execution to minimize stalls. This is another major new paradigm for high performance processing. There are also hints the Cell processor allows this by routing data between various SPEs for example. In comparison, since the Imagine is a pure SIMD machine, inter-cluster communication and kernel execution is always explicit with much lower silicon overhead than a MIMD machine, such as Cell.Recently, CPU vendors have been pushing for multi-core and multi-threading. While this trend is going to be useful for the average user, there's no chance standard CPUs can reach a stream processor's performance. The parallelism between two kernel instances is similar to a thread level parallelism. Each kernel instance gets data parallelism. Inside each kernel, it is still possible to use instruction level parallelism. Task parallelism (such as overlapped I/O) can still happen. It's easy to have thousands of kernel instances but it's simply impossible to have the same amounts of threads. This is the power of the stream.One of the drawbacks of SIMD programming was the issue of Array-of-Structures (AoS) and Structure-of-Arrays (SoA). Programmers often wanted to build data structures with a 'real' meaning, for example:What happened is that those structures were then assembled in arrays too keep things nicely organized. This is AoS. When the structure is laid out in memory, the compiler will produce interleaved data, in the sense that all the structures will be contiguous but there will be a constant offset between, say, the "size" attribute of a structure instance and the same element of the following instance. The offset depends on the structure definition (and possibly other things not considered here such as compiler's policies). There are also other problems. For example, the three position variables cannot be SIMD-ized that way, because it's not sure they will be allocated in continuous memory space. To make sure SIMD operations can work on them, they shall be grouped in a 'packed memory location' or at least in an array. Another problem lies in both "color" and "xyz" to be defined in three-component vector quantities. SIMD processors usually have support for 4-components operations only (with some exceptions however).This kind of problems and limitations made SIMD acceleration on standard CPUs quite nasty. The proposed solution, SoA follows as:For readers not experienced with C, the '*' before each identifier means 'array'. For Java programmers, this is roughly equivalent to "[]". The drawback here is that the various attributes could be spread in memory. To make sure not cause cache misses, we'll have to update all the various "reds", then all the "greens" and "blues". Although this is not so bad after all, it's simply overkill when compared what most stream processors offers.For stream processors, the usage of structures is encouraged. From an application point of view, all the attributes can be defined with some flexibility. Taking GPUs as reference, there is a set of attributes (at least 16) available. For each attribute, the application can state the number of components and the format of the components (but only primitive data types are supported for now). The various attributes are then attached to a memory block, possibly defining a stride between 'consecutive' elements of the same attributes, effectively allowing interleaved data. When the GPU begins the stream processing, it will gather all the various attributes in a single set of parameters (usually this looks like a structure or a "magic global variable"), performs the operations and scatters the results to some memory area for later processing (or retrieving).Summing up, there's more flexibility by application' side yet everything looks very organized on stream processor' side.Historically, CPUs began implementing various tiers of memory access optimizations because of the ever increasing performance when compared to relatively slow growing external memory bandwidth. As this gap widened, big amounts of die area were dedicated to hiding memory latencies. Since fetching information and opcodes to those few ALUs is expensive, very little die area is dedicated to actual mathematical machinery (as a rough estimation, consider it to be less than 10%).A similar architecture exists on stream processors but thanks to the new programming model, the amount of transistor dedicated to management is actually very little.Beginning from a whole system point of view, stream processors usually exist in a controlled environment. GPUs do exist on an add-in board (this seems to also apply to Imagine). CPUs do the dirty job of managing system resources, running applications and such.The stream processor is usually equipped with a fast, efficient, proprietary memory bus (crossbar switches are now common, multi-buses has been employed in the past). The exact amount of memory lanes is dependant on the market range. As this is written, there are still 64bit wide interconnections around (entry-level). Most mid-range models use a fast 128bit crossbar switch matrix (4 or 2 segments), while high-end models deploy huge amounts of memory (actually up to 512MB) with a slightly slower crossbar 256bit wide. By contrast, standard processors from Intel Pentium to some Athlon 64 have only a single 64bit wide data bus.Memory access patterns are much more predictable. While arrays do exist, their dimension is fixed at kernel invocation. The thing which most closely matches a multiple pointer indirection is an indirection chain, which is however guaranteed to finally read or write from a specific memory area (inside a stream).Because of the SIMD nature of the stream processor's execution units (ALUs clusters), read/write operations are expected to happen in bulk, so memories are optimized for high bandwidth rather than low latency (this is a difference from Rambus and DDR SDRAM, for example). This also allows for efficient memory bus negotiations.Most (90%) of a stream processor's work is done on-chip, requiring only 1% of the global data to be stored to memory. This is where knowing the kernel temporaries and dependencies pays.Internally, a stream processor features some communication and management circuits but what's interesting is the Stream Register File (SRF). This is conceptually a large cache in which stream data is stored to be transferred to external memory in bulks. As a cache-like software-controlled structure to the various ALUs, the SRF is shared between all the various ALU clusters. The key concept and innovation here done with Stanford's Imagine chip is that the compiler is able to automate and allocate memory in an optimal way, fully transparent to the programmer. The dependencies between kernel functions and data is known through the programming model which enables the compiler to make an advanced flow analysis and optimally pack the SRFs and automate DMA. The hardware is also able to perform runtime synchronization to allow out-of-order execution of kernel functions. Commonly, this cache and DMA management can take up the majority of a project's schedule, something the stream processor (or at least Imagine) totally automates. Tests done at Stanford showed that the compiler did an as well or better job at scheduling memory than if you handtuned the thing with much effort.There is proof, there can be only a lot of clusters because inter-cluster communication is assumed to be rare. Internally however, each cluster can efficiently exploit a much lower amount of ALUs because inter-cluster communication is common and thus needs to be highly efficient.To keep those ALUs fetched with data, each ALU is equipped with Local Register Files (LRFs), which are basically its usable registers.This three-tiered data access pattern, makes easy to keep temporary data away from slow memories, thus making the silicon implementation highly efficient and power-saving.Although an order of magnitude speedup can easily be expected (even from mainstream GPUs when computing in a streaming manner), not all applications benefit from this. Communication latencies are actually the biggest problem. Although PCI Express improved this with full-duplex communications, getting a GPU (and possibly a generic stream processor) to work will possibly take long amounts of time. This means it's usually counter-productive to use them for small datasets. The stream architecture also incurs penalities for small streams, a behaviour which is officially identified as short stream effect. This basically happens because changing the kernel is a rather expensive operation.Pipelining is a very radicated practice on stream processors, with GPUs featuring pipelines exceeding 200 stages. The cost for switching settings is dependant on the setting being modified but it's now considered to be always expensive. Although efforts are being spent for lowering the cost of switching, it's predictable this isn't going to happen any time soon. To avoid those problems at various levels of the pipeline, many techniques have been deployed such as "〓ber shaders" and "texture atlases". Those techniques are actually game-oriented for the nature of GPUs, but the concepts are interesting for generic stream processing as well. 「イギリス」という通称は、イングランドを表すオランダ語のEngelschまたはポルトガル語のInglesから来ている 普通、各ラングの右端にはコイルを1個配置する オセルタミビルは、インフルエンザ治療薬 エンハンスメントは天然の状態でも起こりうる現象を人為的に似せて行う改良であり、処理石とは見なされないとされる 糸の鉛直方向となす角が θ のとき、おもりの x 軸上にかかわる力 F は、となる モデルTの中古パワーユニットは廉価であったことから、第二次世界大戦以前には、定置動力、小型船舶、小型鉄道車両に好んで転用された歴史がある デザインが鋭角的なものに変更されている 以後の旧ソ連・ロシア原潜（アクラ級、シエラ級）の特徴となる艦尾縦舵上の巨大なソナー・アレイ収納ポッドは、この型ではじめて導入されたもの 心臓形（しんぞうけい）とも呼ばれる アメリカとロシアの軍は、軍事利用目的として、機雷の探索や潜水中の敵の発見と追跡を行うようにハンドウイルカの訓練を行っている 中毒症状は嘔吐・吐き気・手足の痺れなどで、死亡した例もある これは、前述実録の「人心怨叛，與倭同心耳」、「我民亦曰：倭亦人也，吾等何必棄家而避也？」でうかがい知ることができる．また、明の朝鮮支援軍が駆けつけてみると、辺りに散らばる首の殆どが朝鮮の民であったと書かれてある この場合の路線記号は最も主要な部分の路線名に基づいて設定されたと見られるものを使用していることが多い ギアリングは、ローギア約3、ハイギア直結、リバースギア約4であった ルイビルのラジオ放送局は広く多様な音楽及び他の分野を提供している その場合、数千の（離散あるいはアナログの）入出力を持つ Meromはチップセット「Crestine (クレスティーン)」、無線LAN「Kedron (ケドロン)」と共に次世代プラットフォーム「Santa Rosa」を構成する その反応のようすを以下に示す デザインはプロトタイプ・ザクから比べてある程度簡略化されている 1682年（天和2年）に1万石を与えられ、常陸宍戸（茨城県西茨城郡）に陣屋を置いたのに始まる