2e:null 2f:["$","title",null,{"children":"호기심 해결 in 머신러닝: 최신 NVIDIA 그래픽카드 이슈 털기 | Humble Data Miner | Observable"}] 34:T8c1,vikings = { const wrapper = DOM.element('div'); const width = 1000; const height = 500; wrapper.style.width = width + 'px'; wrapper.style.height = height + 'px'; const m = DOM.element('div'); m.id = 'timeline'; m.style.width = (width) + 'px'; m.style.height = height + 'px'; m.style.margin = 'auto'; // needs padding otherwise label optimization will fail m.style.padding = '1px'; wrapper.appendChild(m); const img = await FileAttachment("image.png").url(); const timeline_data = data.filter(d=>d.ReleaseDate>new Date('2019-01-01')).map(d=>({date:d.ReleaseDate.toISOString().split('T')[0],desc:`${d.Abbrv} 출시`})); timeline_data.push({date:'2020-05-13',desc:"https://www.pcworld.com/wp-content/uploads/2023/04/jensen-ampere-100841760-orig.jpg"}); timeline_data.push({date:'2020-05-13',desc:"GTC기조연설에 앞서 세계 최대크기 그래픽카드를 오븐에서 꺼내는 황회장"}) console.log(timeline_data); timeline_data.push({date:'2019-01-09',desc:"https://www.techspot.com/images2/news/bigimage/2022/09/2022-09-24-image-3.jpg"}); timeline_data.push({date:'2019-01-09',desc:"CES2019에서 무어의 법칙의 종말을 재차 강조하는 황회장"}); timeline_data.push({date:'2022-09-24',desc:"https://static.tweaktown.com/news/1x1/90409_nvidia-gtc-2023-keynote-with-ceo-jensen-huang-is-scheduled-for-march-21.jpg"}); timeline_data.push({date:'2022-09-24',desc:"무어의 법칙은 끝났으며 미래에 칩가격이 떨어질일은 없을것이라 말하는 황회장"}); timeline_data.push({date:'2023-03-22',desc:"https://www.eetasia.com/wp-content/uploads/sites/2/2023/05/Press_Image_NVIDIA_Keynote_at_COMPUTEX_hero.jpg"}); timeline_data.push({date:'2023-03-22',desc:"무어의 법칙은 끝났으며 앞으로의 발전은 AI가 주도할것이라 강조하는 황회장"}); // without the timeout observable isn't able to do the label optimization properly setTimeout(() => { const ms = milestones(m) .mapping({ 'timestamp': 'date', 'text': 'desc' }) .parseTime('%Y-%m-%d') .aggregateBy('day') .optimize(true) .distribution('top-bottom') .render(timeline_data); }, 0); return wrapper; }35:Tdfc,{ const height = 500; const margin = { left: 40, right:40, top:30, bottom: 20 }; const svg = d3.create("svg").attr("viewBox", [0, 0, width, height]); const ex = [0,d3.max(data,d=>d.CUDA)]; const x = d3.scaleLinear().domain(ex).range([margin.left, width-margin.right]); const y = d3.scaleLinear().domain([0,d3.max(data,d=>d.MSRP)]).range([height-margin.bottom, margin.top]); svg.append("g").attr("transform",`translate(0,${height-margin.bottom})`).call(d3.axisBottom(x)); svg.append("g").attr("transform",`translate(${margin.left},0)`).call(d3.axisLeft(y).tickFormat(d=>`$${d.toLocaleString()}`)); const arch_cudas = data.reduce((acc,cur)=>{ if(!acc.hasOwnProperty(cur.Architecture)) acc[cur.Architecture] = []; acc[cur.Architecture].push(cur.CUDA); return acc; },{}); const arches = Object.keys(arch_cudas).toSorted(); const ransac_lines = arches.map((k,i)=>{ const x1 = d3.min(arch_cudas[k])-200; const x2 = Math.min(d3.max(arch_cudas[k])+200,ex[1]); const coef = ransac_coef[k]; return {arch:k, x1:x1, x2:x2, y1:coef[0]*x1+coef[1], y2:coef[0]*x2+coef[1], a:coef[0], b:coef[1]}; }); svg.append("g").attr("fill","none").selectAll("line") .data(ransac_lines) .join("line") .attr("stroke",d=>ArchColors[d.arch]).attr("stroke-dasharray","2 2") .attr("x1",d=>x(d.x1)).attr("x2",d=>x(d.x2)).attr("y1",d=>y(d.y1)).attr("y2",d=>y(d.y2)); svg.append("g").attr("font-size",12).selectAll("text") .data(ransac_lines) .join("text") .attr("dominant-baseline","auto").attr("text-anchor","middle") .attr("x",d=>x(d.x2)).attr("y",d=>y(d.y2)).attr("dy",-5) .text(d=>`y=${d.a.toFixed(3)}x${d.b>=0?"+":""}${d.b.toFixed(0)}`); const GFlines = data.reduce((acc,cur)=>{ if(!acc.hasOwnProperty(cur.Series)) acc[cur.Series] = []; acc[cur.Series].push({x:cur.CUDA,y:cur.MSRP}); return acc; },{}); const keys = Object.keys(GFlines).toSorted(); const lines = keys.map((k,i)=>GFlines[k].toSorted((lhs,rhs)=>lhs.x-rhs.x)); const linefunc = d3.line().x(d=>x(d.x)).y(d=>y(d.y)); svg.append("g").attr("fill","none").selectAll("path") .data(lines) .join("path") .attr("stroke", (d,i)=>GFcolors[keys[i]]) .attr("d", d=>linefunc(d)); svg.append("g").selectAll("circle") .data(data) .join("circle") .attr("stroke",d=>GFcolors[d.Series]) .attr("fill", d=>d.inlier?GFcolors[d.Series]:"white") .attr("cx", d=>x(d.CUDA)) .attr("cy", d=>y(d.MSRP)) .attr("r", 3); function placetext(d) { switch(d.Series) { case 10: case 40: d3.select(this).attr("text-anchor","end").attr("dominant-baseline","auto").attr("dx",-1).attr("dy",-1); break; case 16: case 20: case 30: d3.select(this).attr("text-anchor","start").attr("dominant-baseline","hanging").attr("dx",1).attr("dy",1); break; } } svg.append("g").attr("font-size",10).attr("opacity",0.7).selectAll("text") .data(data) .join("text") .attr("x", d=>x(d.CUDA)) .attr("y", d=>y(d.MSRP)) .each(placetext) .text(d=>d.Abbrv); svg.append("g").append("text") .attr("text-anchor","end").attr("dominant-baseline","auto").attr("dx",-4).attr("dy",-4) .attr("x", width-margin.right).attr("y",height-margin.bottom).text("CUDA 코어수"); svg.append("g").append("text") .attr("text-anchor","start").attr("dominant-baseline","hanging").attr("dx",4).attr("dy",4) .attr("x", margin.left).attr("y",margin.top).text("MSRP(제조사 권장 소비자 가격)"); return svg.node(); }36:T771,* ${textcolor("30시리즈",GFcolors[30])} 뒷부분을 제외하면 대체로 정직한 선형관계 아키텍쳐 | CUDA코어 하나당 비용($) | 참고사항 | :-: | --: | :-: | ${textcolor("Pascal",ArchColors["Pascal"])} | 0.197($/코어) | RTX(Tensor,RT코어)가 없음에도 높은 가격 ${textcolor("Turing",ArchColors["Turing"])} | 0.269($/코어) | RTX(Tensor,RT코어)의 초기개발 비용으로 인한 고비용 추정 ${textcolor("Ampere",ArchColors["Ampere"])} | 0.086($/코어) | 가장 저렴한 코어비용, 하지만 코인채굴 활황기에 제 가격구매 불가 ${textcolor("Ada",ArchColors["Ada"])} | 0.098($/코어) | 미세공정과 물류비용등의 상승으로 전세대 대비 코어가격 소폭 상승 * ${textcolor("30시리즈",GFcolors[30])}의 뒷부분 하이엔드 파트(3080Ti~3090Ti)에 과도한 가격 * 추정해볼만한 고가 원인들: 파운더리의 낮은수율, 발열등 → 소량만 양산 * 이때부터 코어 물량전술로 성능향상을 꾀했으나 실패한것으로 추정 * ${textcolor("40시리즈",GFcolors[40])}의 경우 코어 물량전술로 성능향상 성공 * 추정가능한 성공 원인들: 공정미세화의 결과(?) 혹은 파운더리의 양산력(?) * 4080을 제외한 모든 제품이 합리적인 가격(사용한 재료에 비례하는 가격) * 동급의 전세대 가격에 맞출경우 (4060,4060Ti) → 코어수를 줄여 가격맞춤 * ${textcolor("20시리즈",GFcolors[20])}, ${textcolor("16시리즈",GFcolors[16])}는 같은 공정의 ${textcolor("Turing",ArchColors["Turing"])} 아키텍쳐 생산품 * ${textcolor("16시리즈",GFcolors[16])}에는 RTX가 없음에도 ${textcolor("20시리즈",GFcolors[20])}와 유사한 선형관계 * ${textcolor("20시리즈",GFcolors[20])}생산품의 변형제품으로 ${textcolor("16시리즈",GFcolors[16])}를 생산한것으로 추정37:Td0a,{ const height = 500; const margin = { left: 40, right:30, top:30, bottom: 20 }; const svg = d3.create("svg").attr("viewBox", [0, 0, width, height]); const ex = [0,d3.max(data,d=>d.CUDAxGClock)]; const x = d3.scaleLinear().domain(ex).range([margin.left, width-margin.right]); const y = d3.scaleLinear().domain([0,d3.max(data,d=>d.Passmark)]).range([height-margin.bottom, margin.top]); svg.append("g").attr("transform",`translate(0,${height-margin.bottom})`).call(d3.axisBottom(x)); svg.append("g").attr("transform",`translate(${margin.left},0)`).call(d3.axisLeft(y)); const arch_cuclk = data.reduce((acc,cur)=>{ if(!acc.hasOwnProperty(cur.Architecture)) acc[cur.Architecture] = []; acc[cur.Architecture].push(cur.CUDAxGClock); return acc; },{}); const arches = Object.keys(arch_cuclk).toSorted(); const polyreg_lines = arches.map((k,i)=>{ const x1 = d3.min(arch_cuclk[k])-400; const x2 = Math.min(d3.max(arch_cuclk[k])+400,ex[1]); const coef = polyreg_coef[k]; const seq = d3.range(x1, x2+1, (x2-x1)/10).map(x=>[x, x*coef[0]+x*x*coef[1]+coef[2]]); return {arch:k, seq: seq, a:coef[0], a2:coef[1], b:coef[2]}; }); const polyreg_linefunc = d3.line().x(d=>x(d[0])).y(d=>y(d[1])); svg.append("g").attr("fill","none").attr("stroke-dasharray","2 2").selectAll("path") .data(polyreg_lines) .join("path") .attr("stroke", d=>ArchColors[d.arch]) .attr("d", d=>polyreg_linefunc(d.seq)); const GFlines = data.reduce((acc,cur)=>{ if(!acc.hasOwnProperty(cur.Series)) acc[cur.Series] = []; acc[cur.Series].push({x:cur.CUDAxGClock,y:cur.Passmark}); return acc; },{}); const keys = Object.keys(GFlines).toSorted(); const lines = keys.map((k,i)=>GFlines[k].toSorted((lhs,rhs)=>lhs.x-rhs.x)); const linefunc = d3.line().x(d=>x(d.x)).y(d=>y(d.y)); svg.append("g").attr("fill","none").selectAll("path") .data(lines) .join("path") .attr("stroke", (d,i)=>GFcolors[keys[i]]) .attr("d", d=>linefunc(d)); svg.append("g").attr("stroke","none").selectAll("circle") .data(data) .join("circle") .attr("fill", d=>GFcolors[d.Series]) .attr("cx", d=>x(d.CUDAxGClock)) .attr("cy", d=>y(d.Passmark)) .attr("r", 3); function placetext(d) { switch(d.Series) { case 16: case 20: case 30: d3.select(this).attr("text-anchor","end").attr("dominant-baseline","auto").attr("dx",-1).attr("dy",-1); break; case 10: case 40: d3.select(this).attr("text-anchor","start").attr("dominant-baseline","hanging").attr("dx",1).attr("dy",1); break; default: break; } } svg.append("g").attr("font-size",10).attr("opacity",0.7).selectAll("text") .data(data) .join("text") .attr("x", d=>x(d.CUDAxGClock)) .attr("y", d=>y(d.Passmark)) .each(placetext) .text(d=>d.Abbrv); svg.append("g").append("text") .attr("text-anchor","end").attr("dominant-baseline","auto").attr("dx",-4).attr("dy",-4) .attr("x", width-margin.right).attr("y",height-margin.bottom).text("(CUDA × GPU Clock)/1000"); svg.append("g").append("text") .attr("text-anchor","start").attr("dominant-baseline","hanging").attr("dx",4).attr("dy",4) .attr("x", margin.left).attr("y",margin.top).text("PassMark 점수"); return svg.node(); }38:T632,--- ## 참고자료 * 이상치를 걸러내는 선형회귀 알고리즘 * [Wikipedia - RANSAC(RANdom SAmple Consensus)](https://en.wikipedia.org/wiki/Random_sample_consensus) * NVIDIA GeForce 시리즈별 사양 * [Wikipedia - GeForce 40 Series](https://en.wikipedia.org/wiki/GeForce_40_series) * [Wikipedia - GeForce 30 Series](https://en.wikipedia.org/wiki/GeForce_30_series) * [Wikipedia - GeForce 20 Series](https://en.wikipedia.org/wiki/GeForce_20_series) * [Wikipedia - GeForce 16 Series](https://en.wikipedia.org/wiki/GeForce_16_series) * [Wikipedia - GeForce 10 Series](https://en.wikipedia.org/wiki/GeForce_10_series) * PassMark 성능점수 수집 * [PassMark Software - Video Card (GPU) Benchmark - High End Video Cards](https://www.videocardbenchmark.net/high_end_gpus.html) * 황회장님의 발언들 * [PCWorld - Ampere’s coming: Nvidia CEO pulls ‘world’s largest graphics card’ out of an oven before GTC keynote](https://www.pcworld.com/article/399149/amperes-coming-nvidia-ceo-pulls-worlds-largest-graphics-card-out-of-an-oven-before-gtc-keynote.html) * [CNET - CES 2019: Moore's Law is dead, says Nvidia's CEO](https://www.cnet.com/tech/computing/moores-law-is-dead-nvidias-ceo-jensen-huang-says-at-ces-2019/) * [TECHSPOT - Nvidia's Jensen Huang once again claims Moore's Law is dead](https://www.techspot.com/news/96094-nvidia-jensen-huang-once-again-claims-moore-law.html) * [Digitaltrends - Nvidia says falling 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32:{"id":"53ade8720f9a9a26","slug":"looking-into-issues-of-nvidia-graphic-cards-esp-for-40-series","title":"호기심 해결 in 머신러닝: 최신 NVIDIA 그래픽카드 이슈 털기","thumbnail":"f01eb32851cf36b095f473a051a8d1266a686fda8fff3068743b3d2f78418bcc","access_level":"public","create_time":"$D2023-07-31T19:56:12.240Z","update_time":"$D2023-08-04T08:45:44.490Z","view_time":null,"first_public_time":"$D2023-07-31T19:56:12.240Z","has_unpublished_edits":null,"fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"50faa4da52a9ff9b","login":"humble-data-miner","name":"Humble Data Miner","avatar_url":"https://avatars-next.observableusercontent.com/3046b1bea86c5e825baea7c0c403d90ad9690b5396b11b5d02b3a0839fbcb946","tier":"starter_2024"},"version":653,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"4e2fc41e06361cc3","login":"humble-data-miner","name":"Humble Data 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최신 NVIDIA 그래픽카드 이슈 털기\n\n* 최신 NVIDIA 그래픽카드에 대한 관련 질문들\n * 4090 은 정말 싸게나온걸까?\n * 4060, 4060 Ti 등은 정말 비싸게 나온걸까?\n * 30대비 40시리즈의 주된 성능향상은 공정빨일까?\n* 평가 및 분석대상\n 1. 세대별 재료∝가격 평가\n 2. 세대간 (코어×클럭)∝성능 평가","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":347,"value":"---\n## NVIDIA 그래픽카드 출시일을 곁들인 황회장의 발언들","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":352,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":416,"value":"* 2017년부터 재차 **\"무어의 법칙은 죽었다\"** → 할만큼했다. 질적성장 어려웡\n* 오븐에서 큰 그래픽카드를 꺼내는 황회장 → 양적성장(물량)으로 승부 예고\n* 미래에 칩가격이 떨어질일 없다 → 거의 독점상황에서 할인할 이유 없음\n* 앞으로의 발전은 AI가 주도 → 하드웨어 개발은 AI에 관련있을때만 집중","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":21,"value":"---\n## 세대별 재료∝가격 평가\n* 가격산정의 논리(재료의 사용량 ∝ MSRP)를 확인, \"쓴만큼 받는다\"\n * 성능과 관계없이 세대별로 묶어서 집적률과 가격의 관계를 확인\n* 데이터셋: Geforce 그래픽카드 10시리즈 ~ 40시리즈 사양 및 가격\n* 불필요한 특성 제거: CUDA코어수 ∝ ~~TMU,SM수,Tensor코어수,RT코어수~~\n* 결론적으로 CUDA코어수 ∝ MSRP 의 선형관계로 정리가능\n * 이상치를 제거하기 위해 RANSAC 선형회귀 알고리즘 사용","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":24,"value":"{\nfunction add_cuda_multp(v,i) { return v > 0 ? `${v} (x${data[i].CUDA/v})`: v; }\nreturn Inputs.table(data,{\n rows: 14,\n header: {\n Architecture:\"아키텍쳐\",\n Name: \"모델이름\",\n Abbrv: \"이름축약\",\n ReleaseDate: \"출시일\",\n CUDA: \"CUDA코어\",\n TMUs: \"TMU\",\n ROPs: \"ROP\",\n SMcount: \"SM\",\n TensorCores: \"Tensor코어\",\n RTCores: \"RT코어\",\n MemorySize: \"메모리크기\",\n MemoryBus: \"메모리버스\",\n MSRP: \"MSRP\",\n Passmark: \"Passmark 점수\",\n GPUClock: \"GPU Clock(MHz)\",\n },\n columns: [\"Architecture\",\"Name\",\"ReleaseDate\",\"CUDA\",\"TMUs\",\"SMcount\",\"TensorCores\",\"RTCores\",\"MemorySize\",\"MemoryBus\",\"MSRP\"],\n width: {Series:20, Architecture:60,Name:160,ReleaseDate:70, MemorySize:70},\n format: {\n Architecture: (v,i)=> textcolor(v,GFcolors[data[i].Series]),\n TMUs: add_cuda_multp,\n SMcount: add_cuda_multp,\n TensorCores: add_cuda_multp,\n RTCores: add_cuda_multp,\n MemorySize: v=>`${v} GB`,\n MemoryBus: v=>`${v} bit`,\n MSRP: v=>`$${v.toLocaleString()}`,\n }\n});\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":130,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":487,"value":"$36","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":250,"value":"---\n## 세대간 (코어×클럭)∝성능 평가\n* 세대간에 설계가 바뀔때 단일 코어의 성능향상을 확인하기 위함\n* (코어×클럭): 코어수에 클럭스피드를 곱해서 이론적인 성능을 계산\n* (성능): 실제 성능 점수로 PassMark점수를 활용","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":190,"value":"{\nreturn Inputs.table(data,{\n rows: 14,\n maxWidth: 850,\n header: {\n Architecture:\"아키텍쳐\",\n Name: \"모델이름\",\n Abbrv: \"이름축약\",\n ReleaseDate: \"출시일\",\n CUDA: \"CUDA\",\n MemorySize: \"메모리크기\",\n MemoryBus: \"메모리버스\",\n Passmark: \"Passmark 점수\",\n GPUClock: \"GPU Clock\",\n CUDAxGClock: \"(CUDA × GPU Clock) / 1000\",\n },\n columns: [\"Architecture\",\"Name\",\"ReleaseDate\",\"CUDA\",\"GPUClock\",\"CUDAxGClock\",\"Passmark\"],\n width: {Series:20, Architecture:60,Name:160,ReleaseDate:70, CUDA:70, GPUClock:70, Passmark:100 },\n format: {\n Architecture: (v,i)=> textcolor(v,GFcolors[data[i].Series]),\n GPUClock: v=>`${v.toLocaleString()} Mhz`,\n MemorySize: v=>`${v} GB`,\n CUDAxGClock: v=>`${Math.round(v).toLocaleString()}`,\n }\n});\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":218,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":238,"value":"* ${textcolor(\"Turing\",ArchColors[\"Turing\"])} 아키텍쳐 제품에 한해 한시적으로 단일코어 성능 증가\n* ${textcolor(\"Pascal\",ArchColors[\"Pascal\"])}, ${textcolor(\"Ampere\",ArchColors[\"Ampere\"])}, ${textcolor(\"Ada\",ArchColors[\"Ada\"])}의 경우 코어자체 성능(IPC같은)의 질적 개선보다는 코어수와 클럭스피드에 비례한 양적개선으로 성능향상\n * ${textcolor(\"Ampere\",ArchColors[\"Ampere\"])} 주로 코어수를 늘려서 성능향상\n * ${textcolor(\"Ada\",ArchColors[\"Ada\"])} 주로 클럭스피드 늘려서 성능향상 (하이엔드는 코어수도 대거 상향)\n* 무어의 법칙은 죽었고 어쨌든 질보다 양으로 승부?\n * 공정이 미세화할수록 양적향상이 예상되나 가격도 같이 상승\n * 다음세대 50시리즈에서는 얼마나 더 고가에 고전력을 요구할지 기대됨","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":614,"value":"---\n## 답변의 시간\n* 4090은 싼가? 사용된 재료에 알맞은 가격\n* 4060, 4060Ti는 비싼가? 사용된 재료에 알맞은 가격\n* 40시리즈의 성능향상은 공정빨? 대체로 공정빨 (더 많은 코어,더 빠른 클럭스피드)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":652,"value":"---\n## 부연설명 동영상\n\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":622,"value":"$38","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":7,"value":"---\n## Appendix","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":10,"value":"rawdata = await FileAttachment(\"dataset(10,16,20,30,40)_inlier.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"rawdata","database":"$undefined"},{"id":14,"value":"data = {\n const data = rawdata;\n data.forEach((d,i)=>{ d.CUDAxGClock = d.CUDA*d.GPUClock/1000; });\n return data;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":276,"value":"ransac_cuda_msrp = FileAttachment(\"ransac_cuda_msrp.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":297,"value":"ransac_coef = {\n return ransac_cuda_msrp.reduce((acc,cur)=>{ acc[cur.arch]=[cur.a,cur.b]; return acc; },{});\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":284,"value":"linreg_cudagclk_passmark = FileAttachment(\"linreg_deg2_cudagclk_passmark.csv\").csv({typed:true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":329,"value":"polyreg_coef = {\n return linreg_cudagclk_passmark.reduce((acc,cur)=>{ acc[cur.arch]=[cur.a,cur.a2,cur.b]; return acc; },{});\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":110,"value":"GFcolors = ({ 10: d3.schemeCategory10[0], 16: d3.color(d3.schemeCategory10[1]).darker().formatHex(), 20: d3.schemeCategory10[1], 30: d3.schemeCategory10[2], 40: d3.schemeCategory10[3]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":288,"value":"ArchColors = ({Pascal:d3.schemeCategory10[0],Turing:d3.schemeCategory10[1],Ampere:d3.schemeCategory10[2],Ada:d3.schemeCategory10[3]})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":107,"value":"function textcolor(content, style = {}) {\n function yiq(color) {\n const {r, g, b} = d3.rgb(color);\n return (r * 299 + g * 587 + b * 114) / 1000 / 255; // returns values between 0 and 1\n }\n const {\n background,\n color = yiq(background) >= 0.7 ? \"#111\" : \"white\", \n padding = \"0 2px\",\n borderRadius = \"4px\",\n fontWeight = 500,\n ...rest\n } = typeof style === \"string\" ? {background: style} : style;\n return htl.html`${content}`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":16,"value":"html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":357,"value":"stylesheet = html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":344,"value":"milestones = require('d3-milestones')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W39","stdlib":"1","titling":"auto"}} 33:"$32:body"