コンピューティングの基礎と高速化入門

コンピューティングの基礎と処理の高速化入門 #1 at connpass で発表したスライドです。
https://liberal-arts-for-tech.connpass.com/event/123273/

Transcript

1. ίϯϐϡʔςΟϯάͷجૅͱߴ଎Խೖ໳ ࢁా وొ (twitter: @tkclimb0911) April 27, 2019
2. ໨࣍ 1. ίϯϐϡʔλʹΑΔܭࢉͷݪཧ 1.1 ϒʔϧ୅਺ 1.2 ࿦ཧճ࿏ 1.3 ిࢠճ࿏ 1.4 νϡʔϦϯάϚγϯ 1.5 ϊΠϚϯܕίϯϐϡʔλ 2. CPU ʹΑΔܭࢉͷߴ଎Խ 2.1 CPU ͱ͸ 2.2 ISA ͱϚΠΫϩΞʔΩςΫνϟ 2.3 ໋ྩϨϕϧฒྻੑ 2.4 هԱ֊૚ 2.5 σʔλϨϕϧฒྻੑ 3. ίϯύΠϥʹΑΔܭࢉͷߴ଎Խ 3.1 ίϯύΠϥͱ͸ 3.2 ϓϩάϥϜͷදݱͱղऍ 3.3 ίϯύΠϥʹΑΔ࠷దԽ 3.4 ύΠϓϥΠχϯάͱεέδϡʔϦ ϯά 1
3. ίϯϐϡʔλʹΑΔܭࢉͷݪཧ
4. ίϯϐϡʔλγεςϜͷந৅Խ ίϯϐϡʔλ͸ඇৗʹෳࡶͳγεςϜͰ͋Γɺෳ਺ͷϨϕϧͰ༷ʑͳٕज़͕૊Έ߹Θ ͤΒΕͯͰ͖͍ͯΔɻ͜͜Ͱ͸ͦΕΒΛந৅Խͨ͠େ·͔ͳ֊૚ߏ଄Λࣔ͢ɻ Figure 1: ίϯϐϡʔλγεςϜͷந৅Խ [5] 2
5. ίϯϐϡʔλ಺෦Ͱͷ਺஋දݱ: 2 ਐ਺ 2 ਐ਺ (binary) ͸ 17 ੈلʹϥΠϓχοπ͕਺ֶతʹཱ֬͠ɺݱ୅ͷίϯϐϡʔλʹ͓ ͚Δ਺஋දݱʹ༻͍ΒΕ͍ͯΔɻ2 ਐ਺͸͋Δ਺Λ 2 Λج਺ͱͨ͠΂͖ͱ܎਺͔Βͳ Δଟ߲ࣜͰදݱ͢Δɻ ҎԼʹ”147”Λ 10 ਐ਺ (decimal) ͱ 2 ਐ਺ͷ྆ํͰදͨ͠ྫΛࣔ͢ɻ decimal : 14710 = 1 × 102 + 4 × 101 + 7 × 100 (1) binary : 1472 = 1 × 27 + 1 × 24 + 1 × 21 + 1 × 20 (2) ಉ༷ʹɺ10 ਐ਺ͷ 22 Λ 2 ਐ਺ʹม׵͢ΔͱҎԼͷΑ͏ʹͳΔɻ 101102 = 1 × 24 + 0 × 23 + 1 × 22 + 1 × 21 + 0 × 20 = 2210 3
6. 2 ਐ਺ʹΑΔࢉज़: Ճࢉ 2 ਐ਺ʹ͓͍ͯ΋ 10 ਐ਺ͱಉ༷ʹࢉज़ԋࢉΛߦ͏͜ͱ͕Ͱ͖ΔɻҎԼʹՃࢉͷྫΛ ࣔ͢ɻ Figure 2: 10 ਐ਺ͱ 2 ਐ਺ʹΑΔՃࢉͷൺֱ [5] 4
7. 2 ਐ਺ʹΑΔࢉज़: ৐ࢉ Figure 3: 2 ਐ਺ʹΑΔ৐ࢉ [7] 5
8. ϒʔϧ୅਺ ϒʔϧ୅਺͸ 19 ੈلʹδϣʔδɾϒʔϧʹΑΓߟҊ͞Εͨɻ୅਺ܥ (B; +, −, (¯)) ͕ ҎԼͷੑ࣭Λຬͨ࣌͢ɺB Λϒʔϧ୅਺ͱݺͿɻ2 ਐ਺ͷ֤ܻΛϒʔϧ୅਺Λ༻͍ͯ ද͢͜ͱͰɺՃࢉ΍৐ࢉͳͲͷܭࢉΛ୅਺తʹߦ͏͜ͱ͕Ͱ͖Δɻ Figure 4: ϒʔϧ୅਺ͷެཧ (ϋϯςΟϯτϯͷެཧܥ)[12] 6
9. ϒʔϧؔ਺ͱϒʔϧԋࢉ ϒʔϧؔ਺ͱ͸ϒʔϧม਺ B = {0, 1} ͱͦͷ௚ੵ Bn Λ༻͍ͯɺࣸ૾ f : Bn → B Ͱ ఆٛ͞ΕΔɻྫ͑͹ɺf (1, 0, 0, 1, 0) = 1 ͳͲͰ͋Δɻ ϒʔϧԋࢉͱ͸̎ม਺ͷϒʔϧؔ਺ f : B × B → B Ͱ͋ΓɺίϯϐϡʔλʹΑΔ࿦ཧ ԋࢉͱͯ͠΋සൟʹ༻͍ΒΕ͍ͯΔɻҎԼʹϒʔϧԋࢉͷྫΛࣔ͢ɻ Figure 5: ϒʔϧԋࢉͷྫ [12] 7
10. ϒʔϧ୅਺ͱ࿦ཧճ࿏: BUFFER ͱ NOT ϒʔϧԋࢉΛճ࿏ͱͯ͠දݱͨ͠΋ͷΛ࿦ཧճ࿏ͱݺͿɻϒʔϧ୅਺ʹ͓͚Δ೚ҙͷ ԋࢉ͸࿦ཧճ࿏Ͱද͢͜ͱ͕Ͱ͖ɺήʔτͱݺ͹ΕΔɻҎԼʹ߃౳ͱ൱ఆͷϒʔϧԋ ࢉͷਅཧ஋දͱ࿦ཧճ࿏Λࣔ͢ɻ Figure 6: BUFFER ήʔτ [5] Figure 7: NOT ήʔτ [5] 8
11. ϒʔϧ୅਺ͱ࿦ཧճ࿏: AND ͱ OR ओཁͳϒʔϧԋࢉͰ͋Δ࿦ཧੵͱ࿦ཧ࿨ʹ͍ͭͯɺͦͷਅཧ஋දͱදهํ๏ɺ࿦ཧճ ࿏ΛҎԼʹࣔ͢ɻ Figure 8: AND ήʔτ [5] Figure 9: OR ήʔτ [5] 9
12. ϒʔϧ୅਺ͱ࿦ཧճ࿏:XOR, NAND, NOR ͦͷଞͷϒʔϧԋࢉͷਅཧ஋දͱදهํ๏ɺ࿦ཧճ࿏Λࣔ͢ɻ Figure 10: XOR ήʔτ, NAND ήʔτ, NOR ήʔτ [5] 10
13. ిࢠճ࿏ʹΑΔϒʔϧ୅਺ͷදݱ 1937 ೥ʹΫϩʔυɾγϟϊϯ͕ϒʔϧ୅਺ΛεΠονճ࿏ͰදݱͰ͖Δ͜ͱΛূ໌ ͨ͠ [9]ɻεΠονճ࿏͸ϦϨʔͳͲͷిࢠճ࿏ͱͯ͠ߏஙͰ͖ΔͷͰɺϒʔϧ୅਺ ʹ͓͚ΔԋࢉΛ෺ཧతʹܭࢉ͢Δ͜ͱ͕Մೳʹͳͬͨɻ Figure 11: ϦϨʔճ࿏ʹΑΔϒʔϧԋࢉͷදݱ [9] ࣮͸ 1935 ೥ʹதౡষ্͕هͱ౳Ձͳ಺༰Λൃද͍͕ͯͨ͠ɺ࿦จதʹ͸ࢀর͞Εͯ ͍ͳ͍ͱ͞Ε͍ͯΔɻ͜ͷล͸චऀ΋ৄ͘͠ͳ͍ͷͰ͋͘·Ͱࢀߟఔ౓ʹɻ ɻ 11
14. ిѹΛ༻͍ͨ 2 ஋ͷදݱ ϒʔϧԋࢉ͕εΠονճ࿏ͰදݱͰ͖Δ͜ͱΛઆ໌͕ͨ͠ɺҰൠతͳίϯϐϡʔλͷ ͓͍ͯ 2 ஋ͷϒʔϧม਺͸ిѹͷେখͰදݱ͞ΕΔɻ ిѹͷߴ͞ ஋ ిѹ஋ ௿͍ 0 GND ߴ͍ 1 VDD ݱ୅ͷίϯϐϡʔλʹ͓͍ͯɺεΠονճ࿏ͱͯ͠ MOS τϥϯδελ͕༻͍ΒΕͯ ͍ΔɻτϥϯδελΛ༻͍ͨిࢠճ࿏͸ߏ଄͕γϯϓϧͰ৴པੑ͕ߴ͘ɺେྔੜ࢈͕ ՄೳͰ͋Δɻ 12
15. ίϯϐϡʔλΛߏ੒͢Δిࢠૉࢠ: MOS τϥϯδελ MOS τϥϯδελ͸ήʔτɺιʔεɺυϨΠϯͱ͍͏ 3 ͭͷ୺ࢠ͔ΒͳΓɺήʔτ ୺ࢠʹߴిѹΛ༩͑ΔͱιʔεిѹΛυϨΠϯʹग़ྗ͢Δ͜ͱ͕Ͱ͖Δ nMOS ͱ ήʔτ୺ࢠʹ௿ిѹΛ༩͑ͯಉ༷ͷಈ࡞Λ͢Δ pMOS ͕͋Δɻ Figure 12: MOS τϥϯδελͷஅ໘ਤ [8] 13
16. MOS τϥϯδελʹΑΔεΠονͷߏங MOS τϥϯδελ͸ήʔτ୺ࢠͷిѹΛมԽͤ͞Δ͜ͱʹΑͬͯɺιʔεͱυϨΠ ϯؒʹిྲྀ͕ྲྀΕΔͨΊɺ୺ࢠͷిѹΛ੍ޚ͢ΔͨΊͷεΠονͱͯ͠ར༻͢Δ͜ͱ ͕Ͱ͖Δɻ Figure 13: nMOS τϥϯδελͷಈ࡞ [5] 14
17. CMOS ߏ੒ʹΑΔ࿦ཧճ࿏ͷߏங nMOS ͱ pMOS Ͱ࢖͍΍͍͢ిѹ͕ҟͳΔͷͰɺ૒ํΛ૊Έ߹Θͤͯճ࿏Λߏங͢ Δࣄ͕ଟ͍ɻ͜ͷߏ੒Λ CMOS(Complementary MOS) ͱݺͿɻҎԼʹ CMOS Λ༻͍ ͯ NOT, NAND, NOR ͷ࿦ཧήʔτΛߏங͢ΔྫΛࣔ͢ɻ 15
18. ిࢠճ࿏Ͱ 2 ஋Λ༻͍Δ͜ͱͷར఺ ຊདྷ͸࣮਺஋Ͱ͋ΔిѹΛϒʔϧม਺ͱͯ͠ѻ͏͜ͱͰɺిѹͷϊΠζʹରͯ͋͠Δ ఔ౓ͷ଱ੑΛ࣋ͭ͜ͱ͕Ͱ͖ɺ৘ใΛ҆ఆͯ͠఻ୡɾอଘͰ͖Δɻ Figure 15: ిࢠճ࿏ʹΑΔ 2 ਐ਺ͷදݱ [5] 16
19. ϚϧνϓϨΫα 2 ೖྗ͔Β 1 ͭͷೖྗΛબ୒͢Δ࿦ཧճ࿏ΛϚϧνϓϨΫαͱݺͿɻબ୒͸ઐ༻ͷ৴ ߸ S Λ༻͍ͯߦΘΕɺϓϩάϥϜʹ͓͚Δ if จʹ૬౰͢ΔΠϝʔδͰ͋Δɻ Figure 16: ϚϧνϓϨΫαͷ࿦ཧճ࿏ͱਅཧ஋ද [5] 17
20. ॱংճ࿏ͱϑϦοϓϑϩοϓ ͜Ε·Ͱઆ໌ͨ͠ճ࿏͸࣌ؒΛߟྀͤͣɺ༩͑ΒΕͨೖྗΛଈ࠲ʹॲཧ͢Δճ࿏Ͱ͋ ΔͨΊɺ૊Έ߹Θͤճ࿏ͱݺ͹ΕΔɻҰํͰ࿦ཧճ࿏ʹΫϩοΫΛಋೖ͠ɺ࣌ؒʹ· ͕ͨͬͯ஋Λอ࣋͢Δճ࿏Λॱংճ࿏ͱݺͿɻ ೖྗ͞Εͨ஋ΛΫϩοΫ͕ 1 ʹมԽͨ͠ͷΈग़ྗ͢Δॱংճ࿏ΛϑϦοϓϑϩοϓͱ ݺͿɻ͜ͷճ࿏͸ΫϩοΫ͕ 0 ͷ৔߹͸ͦͷ··ͷ஋Λҡ࣋͢Δࣄ͕Ͱ͖ΔɻҎԼʹ D ϑϦοϓϑϩοϓͷྫΛࣔ͢ɻ Figure 17: D ϑϦοϓϑϩοϓͷந৅Խͨ͠ ࿦ཧճ࿏ͱਅཧ஋ද [5] Figure 18: ϑϦοϓϑϩοϓʹΑΔঢ়ଶણҡ [5] 18
21. Ϩδελ N ϏοτͷϨδελͱ͸ N ݸͷ࿦ཧ஋Λอ࣋ͨ͠ந৅తͳهԱૉࢠͷ͜ͱΛࢦ͢ɻ Ұൠతͳίϯϐϡʔλʹ͓͍ͯɺϨδελ͸ڞ௨ͷΫϩοΫೖྗΛ࣋ͭ N ݸͷϑϦο ϓϑϩοϓΛ·ͱΊͨ΋ͷΛར༻͢ΔɻҎԼʹ 4 ϏοτͷϨδελͷྫΛࣔ͢ɻ Figure 19: ϑϦοϓϑϩοϓΛ༻͍ͨ 4 ϏοτϨδελͷྫ [5] 19
22. Ճࢉճ࿏ ҎԼʹՃࢉΛ࣮ߦ͢Δ૊Έ߹Θͤճ࿏Ͱ͋ΔશՃࢉثͷྫΛࣔ͢ɻ͜Ε͸ 1 Ϗοτͷ Ճࢉͷ݁ՌΛ܁Γ্͕Γ਺ͱڞʹग़ྗ͢Δɻෳ਺ฒ΂ͯग़ྗΩϟϦʔ (cout) Λ࣍ͷ ೖྗΩϟϦʔʹ઀ଓ͢Δ͜ͱͰɺଟϏοτͷՃࢉΛ࣮ߦͰ͖Δɻ Figure 20: શՃࢉثͷ࿦ཧճ࿏ [5] ͋ͨɺ1 ͭͷશՃࢉثΛΫϩοΫʹ·͕ͨΓ࢖͍ճ͢͜ͱͰɺଟϏοτͷՃࢉΛߦ͏ ͜ͱ΋Ͱ͖Δɻ͜ͷํ๏͸ԋࢉػ͕ߴՁͩͬͨ࣌୅ʹར༻͞Ε͍ͯͨ (EDVAC ͳͲ)ɻ 20
23. ALU Ճࢉͱ࿦ཧԋࢉ͕ಉҰͷ࿦ཧճ࿏ͰܭࢉͰ͖Δ͜ͱΛར༻ͯ͠ɺͦΕΒΛಉҰͷϢ χοτʹ·ͱΊͨ΋ͷΛ ALU(Arithmetic Logic Unit) ͱݺͿɻ Figure 21: 1bit ALU ͷ࿦ཧճ࿏ [7] 21
24. ଟϏοτΛѻ͏ ALU Figure 22: 64bit ALU ͷ࿦ཧճ࿏ [7] 22
25. νϡʔϦϯάϚγϯ ͜Ε·Ͱ࿦ཧճ࿏ʹΑΓදݱ͞ΕΔϒʔϧ୅਺ͱͦͷԋࢉΛ༻͍Δ͜ͱͰɺ2 ਐ਺ʹ ର͢ΔܭࢉΛߦ͑Δ͜ͱΛઆ໌͕ͨ͠ɺ͜Ε͚ͩͰ͸͋ΒΏΔखଓ͖Λදݱ͢Δ͜ͱ ͸Ͱ͖ͳ͍ɻ͋Δखଓ͖͕ܭࢉՄೳͰ͋Δ͜ͱͷূ໌ͱɺͦΕΛ࣮ݱ͢ΔͨΊͷ࢓૊ Έ͕ඞཁͰ͋Δɻ 1936 ೥ʹΞϥϯɾνϡʔϦϯά͸ਓ͕ܭࢉΛߦ͏ࡍͷखଓ͖Λ෼ੳ͠ɺͦΕͱશ͘ ಉ͜͡ͱΛՄೳʹ͢Δ”ࣗಈػց”ΛఏҊͨ͠ɻࣗಈػց͸਺ཧతख๏Λ༻͍ͯূ໌͞ Ε͓ͯΓɺ ʮνϡʔϦϯάϚγϯʯͱݺ͹Ε͍ͯΔɻ 23
26. νϡʔϦϯάϚγϯͷݪཧ νϡʔϦϯάϚγϯ͸ແݶͷ௕͞Λ࣋ͭςʔϓͱͦΕΛಡΉϔουɺػցΛಈ࡞ͤ͞ Δϧʔϧ͕هड़͞Εͨςʔϒϧ͔ΒͳΔɻςʔϓʹ͸༗ݶݸͷछྨͷه߸ͷ͏ͪ 1 ͭ ͕هࡌ͞Ε͍ͯΔɻ Figure 23: νϡʔϦϯάϚγϯ [2] νϡʔϦϯάϚγϯ͸༗ݶͷঢ়ଶू߹͔Β͋ΔҰͭͷঢ়ଶΛ࣋ͪɺςʔϒϧ͸ঢ়ଶू ߹ͱςʔϓʹهࡌ͞Ε͍ͯΔ༗ݶͳه߸ͷ૊Έ߹Θͤʹରͯ͠ɺ͋Δ໋ྩ͕ఆٛ͞Ε ͍ͯΔɻ΋ͪΖΜɺ༗ݶͳू߹ಉ࢜ͷ૊Έ߹ΘͤͰ͋ΔͨΊɺςʔϒϧʹஔ͚Δ໋ྩ ਺΋·ͨ༗ݶͰ͋Δɻ 24
27. ϑΥϯɾϊΠϚϯܕϚγϯ ݱࡏͷίϯϐϡʔλͷ͓͍ͯɺνϡʔϦϯάϚγϯͰ༻͍ΒΕ͍ͯͨςʔϓɺϔου ͓Αͼςʔϒϧ͸ɺϝϞϦɺϓϩάϥϜΧ΢ϯλɺϓϩηοαͱ࣮ͯ͠૷͞Ε͍ͯ Δɻ͜ͷىݯ͸ 1945 ೥ʹϊΠϚϯɺΤοΧʔτɺϞʔΫϦʔΒʹΑͬͯ࡞ΒΕͨ EDVAC ʹ͋Δͱ͞ΕɺޙʹϊΠϚϯܕίϯϐϡʔλͱݺ͹ΕΔ΋ͷݪܕͰ͋Δ?[3]ɻ ϊΠϚϯܕϚγϯ͸ҎԼͷಛ௃Λඋ͑Δɻ • ໋ྩ͕ஞ࣍తʹ࣮ߦ͞ΕΔ • σʔλ͸ԼͷϏοτ͔Βॱ࣍ϝϞϦ·ͨ͸ϨδελʹऔΓग़͞Εͯॲཧ͞Εɺ࠶ ͼॻ͖໭͞ΕΔ • ֤ԋࢉʹར༻͢ΔԋࢉػߏΛͰ͖Δ͚ͩڞ௨ʹ࢖͏ • ໋ྩͱσʔλΛ۠ผͤͣʹಉ͡ϝϞϦʹ͓͖ɺͲͪΒ΋ԋࢉͷର৅ͱ͢Δ • ϝϞϦʹ͸ΞυϨε͕෇͍͓ͯΓɺͦΕʹΑΓॲཧର৅ͷσʔλΛࢦఆ͢Δ • 1 ͭͷهԱͱ 1 ͭͷϓϩηοαΛ࣋ͭ͜ͱ ϊΠϚϯ͸νϡʔϦϯάϚγϯͷӨڹΛड͚ͯɺ্هΛઃܭͨ͠ͱࢥΘΕΔɻ 25
28. యܕతͳϑΥϯɾϊΠϚϯܕͷϓϩηοα Figure 24: ϑΥϯɾϊΠϚϯܕͷϓϩηοαͷྫ [1] 26
29. CPU ʹΑΔܭࢉͷߴ଎Խ
30. CPU ͱ͸ CPU(Central Processing Unit) ͸Ұൠʹߴີ౓ʹूੵ͞Εͨిࢠճ࿏ (IC) Ͱ͋Γɺࣄ લʹఆΊΕͨ༗ݶछྨͷ໋ྩΛ࣮ߦ͢Δ͜ͱ͕Ͱ͖ΔɻҎԼʹ໋ྩͷྫΛࣔ͢ɻ 1. ࿦ཧԋࢉ: AND, OR ͳͲ 2. جຊతͳࢉज़ԋࢉ: Add, Mul ͳͲ 3. σʔλΛϝϞϦ͔ΒಡΈग़͠·ͨ͸ॻ͖ࠐΈ͢Δ໋ྩ: Load, Store ͳͲ 4. όεΛհͯ͠पลػثΛૢ࡞͢Δ໋ྩ (ϙʔτ I/O ͷ৔߹) ༗໊ͳ CPU ʹ intel ͷ Core γϦʔζɺarm ͷ Conterx-A γϦʔζ౳͕͋Δɻ 27
31. CPU ͷେ·͔ͳߏ੒ Figure 25: ૉ๿ͳ CPU ͷߏ੒ [7] 28
32. ԋࢉػͱϨδελ CPU ಺෦ʹ͸࣮ࡍʹԋࢉΛߦ͏ϢχοτͱଟϏοτͷϨδελ͕ز͔ͭ౥ࡌ͞Εͯ ͍Δɻ͜Ε͸Ұ࣌తʹσʔλΛอଘ͢ΔͨΊͷిࢠճ࿏Ͱ͋ΓɺͦΕͧΕ൪߸͕ৼΒ Ε͍ͯΔɻ൪߸͸ޙʹઆ໌͢Δػցޠͷ಺෦ͰҾ਺ͱͯ͠ར༻͞ΕΔɻ Figure 26: ԋࢉػͱϨδελ [7] 29
33. ISA: Instruction Set Architecture CPU Ͱ࣮ߦͰ͖Δ໋ྩΛ·ͱΊͨ΋ͷͷ͜ͱΛ ISA ͱݺͿɻISA ͸ιϑτ΢ΣΞͱ ϋʔυ΢ΣΞͷΠϯλʔϑΣΠεͰ͋ΓɺϓϩάϥϚ͔ΒݟͨϓϩηοαͷΞʔΩς ΫνϟͱΈͳ͢͜ͱ΋Ͱ͖Δɻ௨ৗ͸ϓϩάϥϚ͕ίϯϐϡʔλΛ੍ޚ͢Δࡍʹ༻͍ Δࣄ͕Ͱ͖Δ࠷΋௿Ϩϕϧͳ”ιϑτ΢ΣΞ”Ͱ΋͋Δɻ ISA ʹରͯ͠ɺISA Λ࣮ߦ͢ΔͨΊͷ಺෦ͷ࿦ཧճ࿏ΛϚΠΫϩΞʔΩςΫνϟ (march) ͱݺͼɺ͜Ε͸ϓϩηοαΛ಺෦͔ΒݟͨࡍͷΞʔΩςΫνϟͱ͍͏͜ͱ͕ Ͱ͖Δɻ ൚༻ੑͷ؍఺͔ΒҟͳΔϕϯμ͕ಉҰͷ ISA Λ࠾༻͍ͯ͠Δ CPU ΋͋Δɻྫ͑͹ɺ intel ࣾͱ AMD ࣾ͸ޓ͍ʹ͋Δఔ౓ͷޓ׵ੑ͕͋Δ ISA Λ࠾༻͍ͯ͠Δ͕ɺͦͷ march ͸શ͘ҟͳΔɻ ຊεϥΠυͰ͸ ISA ͱͯ͠ RISC-V ͱ x86 Λ༻͍ͯઆ໌Λߦ͏ɻ 30
34. ISA ͷߏ੒ ISA ͷ໋ྩܗࣜͷྫͱͯ͠ɺRISC-V RV32I R ܗࣜΛ঺հ͢Δɻ Figure 27: RISC-V ISA ͷϑΟʔϧυߏ੒ [7] • opcode: جຊతͳ໋ྩͷछྨΛද͢ • rd: ܭࢉͷ݁Ռ͕ॻ͖ࠐ·ΕΔϨδελ (destination) • rs1: ୈ 1 ΦϖϥϯυͱͳΔϨδελ • rs2: ୈ 2 ΦϖϥϯυͱͳΔϨδελ • funct7: ௥Ճ৘ใΛ༩͑ΔͨΊͷϑΟʔϧυ x5 = x6 + x7 -> 0000000 , 00111 , 00110 , 000, 00101 , 0110011 31
35. Figure 28: RISC-V ͷ ISA[7] 32
36. ػցޠͱΞηϯϒϦݴޠ ISA Λ࣮ࡍʹίϯϐϡʔλ͕ղऍͰ͖ΔΑ͏ʹ 2 ਐ਺ʹΤϯίʔυͨ͠΋ͷΛػցޠ ͱ͍͏ɻػցޠ͸όΠτίʔυͰ͋ΔͨΊਓ͕ؒಡΉ͜ͱʹ޲͍͍ͯͳ͍ɻैͬͯɺ ػցޠΛਓ͕ؒಡΈ΍͍͢ܗʹʢ΄΅ʣ1 ର 1 Ͱஔ͖׵͑ͨ؆୯ͳϓϩάϥϛϯάݴ ޠͰ͋ΔΞηϯϒϦݴޠΛਓؒ͸ར༻͢Δɻ͜ΕΒ͸Ξηϯϒϥͱݺ͹ΕΔιϑτ ΢ΣΞΛ௨ͯ͠ػցޠʹม׵͞ΕΔɻ 33
37. ػցޠͱΞηϯϒϦݴޠͷྫ: x86 ҎԼʹɺ2 ม਺ΛՃࢉ͢Δؔ਺ add Λ࣮ߦ͢Δ x86 ͷػցޠͱΞηϯϒϦݴޠ (GAS) Λࣔ͢ɻx86 ͷ CISC Ͱ͋Γɺ໋֤ྩͷ௕͕͞౳͘͠ͳ͍͜ͱ͕෼͔Δɻ int add(int a, int b) { return a + b; } _add: 55 // pushq %rbp 48 89 e5 // movq %rsp , %rbp 8d 04 37 // leal (%rdi ,%rsi), %eax 5d // popq %rbp c3 // retq 34
38. ϚΠΫϩΞʔΩςΫνϟ ISA ͕ϓϩάϥϚ੍͕ޚՄೳͳ࠷΋௿Ϩϕϧͳιϑτ΢ΣΞͰ͋ΔҰํͰϚΠΫϩ ΞʔΩςΫνϟ͸ͦͷ࣮૷Ͱ͋Δϋʔυ΢ΣΞࣗମͷߏ੒Λࢦ͠ɺճ࿏ਤ΍ RTL ͷ ܗͰදݱ͞ΕΔɻ࣍ϖʔδʹɺRISC-V ISA Λ࣮ߦ͢ΔͨΊͷૉ๿ͳ CPU ͱ intel x86 ίΞͷϚΠΫϩΞʔΩςΫνϟͷྫΛࣔ͢ɻ 35
39. ϚΠΫϩΞʔΩςΫνϟͷྫ Figure 29: Intel Core i7 ͷ march[6] Figure 30: ARM A53 ͷ march[6] 36
40. ISA ͕ CPU Ͱ࣮ߦ͞ΕΔ࢓૊Έ: ࢉज़໋ྩ Ճࢉ໋ྩ͕࣮ߦ͞ΕΔࡍͷ CPU(σʔλόε) ͷ༷ࢠΛࣔ͢ɻ // ̎ͭͷϨδελͷ஋ΛՃࢉ͢Δ add x1 , x2 , x3 // a = b + c 1. ໋ྩ͕ϑΣον͞ΕɺPC ͕Ճࢉ͞ΕΔ 2. ໋ྩ͕σίʔυ͞Εɺ֤Ϣχοτʹ৴߸͕ग़ྗ͞ΕΔ 3. ্هͷ৴߸ʹैͬͯɺϨδελϑΝΠϧ͔Β x2 ͱ x3 ͷσʔλ͕ಡΈग़͞ΕΔ 4. ಡΈग़͞ΕͨσʔλΛ ALU ͰՃࢉ͢Δ͞ΕΔ 5. ALU ͷग़ྗ͕ϨδελϑΝΠϧ಺ͷ x3 ʹॻ͖ࠐ·ΕΔ 37
41. Ճࢉ໋ྩ͕ CPU Ͱ࣮ߦ͞ΕΔ࢓૊Έ Figure 31: ૉ๿ͳ CPU ʹΑΔՃࢉ໋ྩͷ࣮ߦ 38
42. ISA ͕ CPU Ͱ࣮ߦ͞ΕΔ࢓૊Έ: ϝϞϦ໋ྩ ϩʔυ໋ྩ͕࣮ߦ͞ΕΔࡍͷ CPU(σʔλόε) ͷ༷ࢠΛࣔ͢ɻ // ഑ྻ͔Β஋ΛϨδελʹϩʔυ͢Δ lw x1 , 4(x6) // x = a[i + 1] 1. ໋ྩ͕ϑΣον͞ΕɺPC ͕Ճࢉ͞ΕΔ 2. ໋ྩ͕σίʔυ͞Εɺ֤Ϣχοτʹ৴߸͕ग़ྗ͞ΕΔ 3. Ϩδελ x1 ͷσʔλ͕ϨδελϑΝΠϧ͔ΒಡΈग़͞ΕΔ 4. ಡΈग़͞Εͨ஋ͱఆ਺஋ (4) Λ ALU ͰՃࢉ͢Δ (ΞυϨεͷܭࢉ) 5. ্هͷग़ྗ͞ΕͨΞυϨε͔ΒɺDataMemory ͕ϝϞϦ͔Βσʔλϩʔυ͢Δ 6. DataMemory ͷग़ྗ͕ϨδελϑΝΠϧ಺ͷ x5 ʹॻ͖ࠐ·ΕΔ 39
43. ϩʔυ໋ྩ͕ CPU Ͱ࣮ߦ͞ΕΔ࢓૊Έ Figure 32: ૉ๿ͳ CPU ʹΑΔϩʔυ໋ྩͷ࣮ߦ 40
44. ISA ͕ CPU Ͱ࣮ߦ͞ΕΔ࢓૊Έ: ϒϥϯν໋ྩ ϒϥϯν໋ྩ͕࣮ߦ͞ΕΔࡍͷ CPU(σʔλόε) ͷ༷ࢠΛࣔ͢ɻ // ̎ͭͷม਺Λൺֱͯ͠ɺ౳͚͠Ε͹෼ذ͢Δ beq x1 , x2 , offset // if (x1 == x2) pc += offset; 1. ໋ྩ͕ϑΣον͞ΕɺPC ͕Ճࢉ͞ΕΔ 2. ໋ྩ͕σίʔυ͞Εɺ֤Ϣχοτʹ৴߸͕ग़ྗ͞ΕΔ 3. Ϩδελ x1 ͱ x2 ͷσʔλ͕ϨδελϑΝΠϧ͔ΒಡΈग़͞ΕΔ 4. PC ͷ஋ͱ offset ͷ஋Λ 2 ഒͯ͠ූ߸֦ுͨ͠΋ͷΛ ALU ͰՃࢉ͢Δ 5. x1 ͱ x2 ͷ஋͕౳͚͠Ε͹ɺ݁ՌΛ PC ʹ্ॻ͖͢Δ ্هͰՃࢉ͞ΕΔ offset ͸ 12bit Ͱ͋Γɺͦͷ஋Λ 2 ഒ͢Δͷ͸໋ྩ௕͕ 2 όΠτͷ ഒ਺Ͱ͋Δඞཁ͕͋Δͱ͍͏ RISC-V ͷ࢓༷Ͱ͋Δɻ 41
45. ϒϥϯν໋ྩ͕ CPU Ͱ࣮ߦ͞ΕΔ࢓૊Έ Figure 33: ૉ๿ͳ CPU ʹΑΔϒϥϯν໋ྩͷ࣮ߦ 42
46. CPU શମͷঢ়ଶભҠͷྫ Figure 34: CPU શମͷঢ়ଶભҠͷྫ [1] 43
47. ओཁͳ ISA • x86 Ұൠ޲͚ͷίϯϐϡʔλ͔ΒϫʔΫεςʔγϣϯ·Ͱ෯޿͘ར༻͞Ε͍ͯΔɻ • ARM ଟ͘ͷϞόΠϧσόΠε΍ϚΠίϯɺ૊ΈࠐΈػثͳͲʹ޿͘ར༻͞Ε͍ͯΔɻ e.g. εϚʔτϑΥϯ֤छɺRapberryPiɺNintendo DS ͳͲɻ • RISC-V 2010 ೥ʹ UCB ͔ΒΦʔϓϯιʔεͱͯ͠ൃදɻ࠷ۙ੝Γ্͕͖͍ͬͯͯΔɻ • MIPS ήʔϜػ΍૊ΈࠐΈػثͳͲʹར༻͞Ε͍ͯΔɻ e.g. PlayStationɺNintendo 64 ͳͲ • PowerPC ਾ͑ஔ͖ܕήʔϜ΍εʔύʔίϯϐϡʔλ޲͚ʹར༻͞Ε͍ͯΔɻ e.g. IBM ͷϫʔΫεςʔγϣϯɺچ MacintoshɺPlayStation3ɺXbox360 ͳͲɻ 44
48. CPU ʹ͓͚Δ଎͞ͱ͸ʁ: CPI CPU Ͱ໋ྩΛ࣮ߦ͢ΔࡍʹɺͲΕ͘Β͍ΫϩοΫαΠΫϧ͕ඞཁͰ͋Δ͔Λࣔͨ͠ ਺஋Λ CPI ͱݺͿɻ͜Ε͸ ISA ʹର֤ͯ͠ϚΠΫϩΞʔΩςΫνϟ͕Ͳͷ͘Β͍ߴ଎ ʹಈ࡞͢ΔΛද͢ج४ͱͳΔɻ ϓϩάϥϜʹ͓͚Δ໋ྩ i ͷ࣮ߦ਺ ICi ɺ໋ྩ i ʹඞཁͳΫϩοΫαΠΫϧ਺Λ CPIi ͱ͢ΔͱɺCPI ͸ҎԼͷΑ͏ʹܭࢉͰ͖Δɻ CPI = n ∑ i=1 ICi IC × CPIi CPI Λ༻͍Δ͜ͱͰɺҟͳΔ ISA ΍ϚΠΫϩΞʔΩςΫνϟͷ࣮૷Λൺֱ͢Δ͜ͱ͕ Ͱ͖Δɻͨͩ͠ɺύΠϓϥΠϯԽ͞Ε͍ͯΔͱ͜ͷ਺஋Λ۩ମతʹٻΊΔ͜ͱ͸೉ ͍͠ɻ 45
49. CPI ܭࢉͷ۩ମྫ ҎԼͷ৚݅ͷϓϩάϥϜͱϓϩηοαʹ͓͍ͯɺCPI(CPIbase) ΛٻΊͯΈΔɻ • ුಈখ਺఺ԋࢉͷׂ߹ Freqfp = 25 ˋ • ුಈখ਺఺ԋࢉͷฏۉ CPI CPIfp = 4.0 • ଞͷԋࢉͷฏۉ CPI CPIothers = 1.33 • ฏํࠜԋࢉͷׂ߹ Freqsqrt = 2 ˋ • ฏํࠜԋࢉͷ CPI CPIsqrt = 20 CPIbase = (CPIfp × Freqfp) + (CPIothers × 1 − Freqfp) (3) = (4 × 0.25) + (1.33 × 0.75) = 2.0 (4) 46
50. CPI ܭࢉͷ۩ମྫ ҎԼͷ̎ͭͷϓϩηοαͷઃܭύλʔϯʹ͍ͭͯɺͲͪΒ͕ΑΓੑೳΛ޲্ͤ͞Δ͔ Λߟ͑ͯΈΔɻ 1. ฏํࠜԋࢉͷ CPI Λ 2 ʹݮগͤ͞Δ (CPI1) 2. શͯͷුಈখ਺఺ԋࢉͷ CPI Λ 2.5 ʹݮগͤ͞Δ (CPI2) ৽͍͠ฏํࠜԋࢉͷ CPI Λ CPInews qrt ɺ৽͍͠ුಈখ਺఺ԋࢉͷ CPI Λ CPInewf p ͱ͢ ΔͱɺͦΕͧΕҎԼͷΑ͏ʹܭࢉͰ͖Δɻ CPI1 = CPIbase − Freqsqrt × (CPIsqrt − CPInews qrt) (5) = 2.0 − 0.02 × (20 − 2) = 1.64 (6) CPI2 = (CPI1−Freqfp × CPIothers) + (Freqfp × CPInewf p) (7) = (0.75 × 1.33) + (0.25 × 2.5) = 1.625 (8) 47
51. ໋ྩϨϕϧฒྻੑ ͜Ε·Ͱ CPU ΞʔΩςΫτ͸୯Ұͷϓϩηοαͷ্Ͱز͔ͭͷ໋ྩΛฒྻʹ࣮ߦ͢ Δ͜ͱʹΑͬͯੑೳΛ޲্͖ͤͯͨ͞ɻ͜ΕΒ͸໋ྩͷ࣋ͭજࡏతͳॏෳΛར༻ͯ͠ ଎౓Λ޲্͓ͤͯ͞Γɺ͜ΕΛ໋ྩϨϕϧฒྻੑ (ILP) ͱݺ͹ΕΔɻ(ILP: Instruction Level Parallelism) ࣍ʹ໋ྩϨϕϧฒྻੑͷྫͰ࠷΋ҰൠతͳྫͰ͋ΔύΠϓϥΠϯʹ͍ͭͯઆ໌͢Δɻ 48
52. ߴ଎Խͷྫ 2: ύΠϓϥΠϯ γϯάϧαΠΫϧߏ੒͸શͯͷ໋ྩ͕ऴྃ͢Δ·Ͱʹ௕͍͕͔͔࣌ؒΓɺ֤ܭࢉࢿݯ ͷར༻཰͕Լ͕ͬͯ͠·͏໰୊͕͋ͬͨɻݱ୅ͷ CPU ͸ύΠϓϥΠϯͱ͍͏ߏ଄Λ ༻͍ͯɺ͜ͷ໰୊Λղܾ͠Α͏ͱ͍ͯ͠Δɻ͜Ε͸໋֤ྩͷ࣋ͭॏෳੑΛར༻ͯ͠ɺ ͦΕͧΕΛฒྻʹ࣮ߦ͢Δ͜ͱʹΑͬͯॲཧΛߴ଎ԽΛ͢Δٕज़Ͱ͋Δɻ ҎԼͷચ୕ͷྫ͔Β෼͔ΔΑ͏ʹɺҰͭͷॲཧΛز͔ͭͷεςʔδʹ෼ׂ͠ɺҰͭͷ ॲཧͷ࣮ߦதʹۭ͍͍ͯΔࢿݯΛ࣍ͷॲཧʹར༻͢Δ͜ͱͰɺશମͱͯ͠୹͍࣌ؒͰ ॲཧ͢Δ͜ͱͰ͖Δɻ Figure 35: ύΠϓϥΠϯΛར༻ͨ͠ચ୕ͷޮ཰Խ [7] 49
53. γϯάϧαΠΫϧͱύΠϓϥΠϯߏ੒ͷ໋ྩ࣮ߦ࣌ؒͷൺֱ ֤εςʔδͷ࣮ߦ࣌ؒ͸ۉ౳Ͱ͋Δ΄Ͳɺ໋ྩͷ࣮ߦ଎౓͕޲্͢Δࣄ͕෼͔Δɻ Figure 36: γϯάϧαΠΫϧͱύΠϓϥΠϯߏ੒ͷ࣮ߦͷ༷ࢠ [7] 50
54. ύΠϓϥΠϯΛಋೖͨ͠ϚΠΫϩΞʔΩςΫνϟͷྫ ֤ϢχοτͷؒʹϨδελΛڬΈࠐΉ͜ͱͰύΠϓϥΠϯΛ࣮૷͢Δ͜ͱ͕Ͱ͖Δɻ Figure 37: ૉ๿ͳ CPU ͷύΠϓϥΠϯԽ [7] 51
55. ύΠϓϥΠϯʹ͓͚Δ໰୊: ϋβʔυ ύΠϓϥΠϯΛ༻͍ͯ΋ৗʹຖαΠΫϧ໋ྩΛ࣮ߦͰ͖ΔΘ͚Ͱ͸ͳ͍ɻ͜Ε͸໋ྩ ؒʹ͓͚ΔσʔλͷґଘͳͲ͕ݪҼͰ࣍ͷ໋ྩ͕࣍ͷΫϩοΫαΠΫϧͰ࣮ߦͰ͖ͳ ͍৔߹͕͋ΔͨΊͰ͋Δɻ͜ΕΒ͸ϋβʔυͱݺ͹ΕɺύΠϓϥΠϯͷԁ׈ͳ࣮ߦΛ ཚͯ͠εϧʔϓοτΛ௿Լͤ͞ΔɻҎԼʹओཁͳϋβʔυΛ̏ͭ঺հ͢Δɻ 1. σʔλϋβʔυ 2. ੍ޚϋβʔυ 3. ߏ଄ϋβʔυ 52
56. σʔλϋβʔυ σʔλϋβʔυ͸࿈ଓ͢Δ໋ྩͷΦϖϥϯυ͕લͷ໋ྩͷ݁Ռʹґଘ͍ͯ͠Δ৔߹ʹ ى͜ΔɻҎԼ͸̎ͭ໨ͷ sub ໋ྩ͕લͷ add ໋ྩͷ݁Ռʹґଘ͍ͯ͠ΔͨΊɺRF ε ςʔδͰཹ·͍ͬͯΔྫͰ͋Δɻ͜ͷ৔߹ɺadd ໋ྩ͕ WB εςʔδʹ౸ୡ͠ͳ͍ͱ sub ໋ྩ͸࣮ߦͰ͖ͳ͍ɻ͜ͷΑ͏ʹύΠϓϥΠϯ͕ఀࢭ͍ͯ͠ΔαΠΫϧΛύΠϓ ϥΠϯετʔϧ (όϒϧ) ͱݺͿ add x5 , x0 , x1 sub x2 , x5 , x3 ɹɹɹ Figure 38: ύΠϓϥΠϯʹ͓͚Δσʔλϋβʔυͷྫ 53
57. σʔλϋβʔυͷվળํ๏ σʔλϋβʔυͷྫ͸લͷ໋ྩʹඞཁͳεςʔδ (EX) ΑΓޙͷεςʔδ (WD) Ͱ͠ ͔ɺͦͷ݁Ռ͕Ϩδελʹॻ͖ࠐ·Εͳ͍͜ͱ͕ݪҼͰى͜Δɻ ͜Ε͸ඞཁͳεςʔδ͕ऴΘͬͨ࣌఺Ͱͦͷ݁ՌΛઌऔΓ͢ΔύεΛ࡞Δ͜ͱͰվળ Ͱ͖Δɻ͜ΕΛϑΥϫʔσΟϯάͱݺͿɻ͔͠͠ɺ͜ͷख๏Ͱ׬શʹετʔϧΛආ͚ ΒΕΔΘ͚Ͱ͸ͳ͘ɺϩʔυ໋ྩ͔Β࣍ͷ໋ྩͷΦϖϥϯυΛऔಘ͢Δ৔߹͸গ਺ͩ ͕ετʔϧ͕ൃੜ͢Δʢϩʔυ஗Ԇʣ ɻ Figure 39: ϑΥϫʔσΟϯάʹΑΔσʔλϋ βʔυͷվળ Figure 40: ϑΥϫʔσΟϯάͱϩʔυ஗Ԇ 54
58. ੍ޚϋβʔυ ੍ޚϋβʔυ͸ɺ৚݅෼ذ໋ྩ͕࣮ߦ͞ΕΔ৔߹ʹͦͷܭࢉ͕ऴྃ͢Δ·Ͱ࣍ͷ໋ྩ ͷΞυϨεΛ஌Δ͜ͱ͕Ͱ͖ͳ͍ͨΊʹൃੜ͢Δɻ ࠷΋ૉ๿ͳ੍ޚϋβʔυ΁ͷରࡦ͸ɺ৚݅෼ذ໋ྩͷܭࢉ͸ EX Ͱऴྃ͢Δ·Ͱɺ࣍ ͷ໋ྩͷϑΣονΛετʔϧ͢Δ͜ͱͰ͋Δɻ͔͜͠͠ͷख๏͸ύΠϓϥΠϯͷஈ਺ ͕૿͑Δ΄Ͳɺ஗Ԇ͕େ͖͘ͳΔɻ Figure 41: ύΠϓϥΠϯʹ͓͚Δ੍ޚϋβʔυͷྫ 55
59. ੍ޚϋβʔυͷվળํ๏ ৚݅෼ذ໋ྩͷ෼ذ͕੒ཱ͠ͳ͍৔߹ɺ࣍ͷ໋ྩ͸ϑΣονΛ෼ذ໋ྩͷ EX ·Ͱ଴ ͭඞཁ͸ͳ͍ɻैͬͯɺ৚݅෼ذ͕੒ཱ͠ͳ͍ͱԾఆͯ͠௨ৗ௨ΓʹύΠϓϥΠϯΛ ಈ࡞ͤ͞ɺ෼ذ͕ൃੜ͢Δ͜ͱ͕Θ͔ͬͨॠؒʹ݁ՌΛ࡟আ (ϑϥογϡ) ͢Δख๏ ͕͋Δɻ͜Ε͸୯ʹετʔϧͤ͞Δख๏ΑΓฏۉͯ͠൒෼ͷαΠΫϧ਺Λઅ໿Ͱ͖Δ ͕ɺϨδελͷ಺༰Λ࡟আ͢ΔͨΊͷࢿݯ͕ඞཁͰ͋Δɻ Figure 42: ϑϥογϡʹ͓͚Δ੍ޚϋβʔυͷվળ 56
60. ߏ଄ϋβʔυ ߏ଄ϋβʔυͱ͸ύΠϓϥΠϯͷ 2 ͭҎ্ͷεςʔδ͕ 1 ͔ͭ͠ແ͍ܭࢉࢿݯΛऔΓ ߹͏͜ͱͰൃੜ͢ΔϋβʔυͰ͋Δɻ RISC ͸શͯͷεςʔδ͕ॱʹ࣮ߦ͞ΕΔͨΊɺ໋ྩؒͰܭࢉࢿݯͷऔΓ߹͍͕ى͜ Δ͜ͱ͕গͳ͘ɺߏ଄ϋβʔυ͸͋·Γ໰୊ͱͳΒͳ͍ɻ͔͠͠ɺ໋ྩͱσʔλͷϝ ϞϦΞΫηεʹಉҰͷܦ࿏Λ༻͍͍ͯΔ৔߹ɺσʔλͷϝϞϦΞΫηεΛ༏ઌͯ͠ޙ ଓ໋ྩͷϑΣονΛ஗Ԇͤ͞ͳ͚Ε͹ͳΒͳ͍ΞʔΩςΫνϟ΋͋Δɻ Figure 43: ύΠϓϥΠϯʹ͓͚Δߏ଄ϋβʔυͷྫ 57
61. ϝϞϦΞΫηε࣌ؒͷ໰୊ ϓϩηοαͷ಺෦ͷߏ੒͚ͩͰ͸ͳ͘ɺ֎෦ͱͷ௨৴࣌ؒ΋·࣮ͨߦ଎౓ʹେ͖ͳӨ ڹΛ༩͑Δɻ௨ৗɺओهԱ͸Ϩδελͱൺ΂ͯඇৗʹ௿଎Ͱ͋Γɺͦͷ଎౓ࠩ͸໋ྩ ϑΣον΍ϝϞϦ໋ྩͷࡍʹେ͖ͳετʔϧΛҾ͖ىͯ͜͠଎౓Λ௿Լͤ͞Δɻ͞Β ʹɺϑΝΠϧͳͲͷσʔλ͸σΟεΫʹอଘ͞Ε͓ͯΓɺ͜ΕΒ͸ओهԱΑΓ΋େ༰ ྔͰ͋Δ͕௿଎Ͱ͋ΔͨΊΞΫηεʹ͸ߋʹ௕͍͕͔͔࣌ؒΔɻ Figure 44: ֤هԱ૷ஔͷ֓ཁ [6] 58
62. هԱ֊૚ ϝϞϦΞΫηε࣌ؒͷ໰୊Λվળ͢ΔͨΊʹɺ௨ৗͷ CPU Ͱ͸ෳ਺ͷهԱ૷ஔΛ֊ ૚Խͯ͠഑ஔ͢Δߏ੒Λͱ͍ͬͯΔɻ͜Ε͸খ༰ྔͰߴ଎ͳϝϞϦΛ্Ґʹɺେ༰ྔ ͕ͩ௿଎ͳ΋ͷԼҐʹ഑ஔͯ͠ɺ্͔ΒॱʹΞΫηεΛߦ͏ɻར༻ස౓ͷߴ͍σʔλ Λ্Ґʹ͓͘͜ͱͰશମͱͯ͠ΞΫηε࣌ؒΛ୹ॖͰ͖Δɻ Figure 45: هԱ֊૚ͷ֓೦ [4] 59
63. ߴ଎Խͷྫ 2: Ωϟογϡ ϨδελͱओهԱͷؒʹ഑ஔ͞ΕͨهԱ૷ஔΛΩϟογϡͱݺͼɺҰൠతʹ͸ SRAM Ͱߏ੒͞Εͨখ༰ྔͳϝϞϦΛ 2 ͔Β 3 ͭ΄Ͳ֊૚Խͨ͠΋ͷͰ͋Δɻϓϩηοα͸ ϝϞϦΞΫηε͕ൃੜͨ͠৔߹ɺ·ͣΩϟογϡʹΞΫηεͯͦ͠ͷσʔλ͕͋Δ͔ ໰͍߹ΘͤΔɻ΋͠σʔλ͕͋Ε͹Ωϟογϡ͸ΛΕΛฦ٫͠ (ώοτ)ɺͳ͍৔߹͸ ΑΓԼҐͷهԱ֊૚ʹ໰͍߹ΘͤΔ (ϛεʣ ɻ Figure 46: Ωϟογϡ΁ͷΞΫηε [1] 60
64. Ωϟογϡͷߏ੒ Ωϟογϡ͸ϝϞϦͱಉ༷ʹৼΔ෣͏ඞཁ͕͋ΔͨΊͦͷσʔλ͸ΞυϨεͰ؅ཧ͞ ΕΔɻ͔͠͠ɺߴՁͳهԱૉࢠͰߏ੒͞ΕΔͨΊɺอ࣋͢Δσʔλ͸ޮ཰తʹબ୒͢ Δඞཁ͕͋ΔɻैͬͯɺҎԼͷϓϩάϥϜʹ͓͚ΔओཁͳϝϞϦΞΫηεύλʔϯΛ ੜ͔ͯ͠ઃܭ͞ΕΔɻ • ࣌ؒతہॴੑ • ۭؒతہॴੑ جૅతͳ̏ͭͷΩϟογϡͷߏ੒ํࣜΛҎԼʹࣔ͢ɻ • μΠϨΫτϚοϐϯάํࣜ • ϑϧΞιγΞςΟϒํࣜ • N ΢ΣΠ-ηοτΞιγΞςΟϒํࣜ ࣌ؒͷ౎߹্ɺͦΕͧΕͷৄࡉ͸লུ͢Δɻ 61
65. Ωϟογϡ׆༻ͷྫ: ߦྻੵ C++ʹΑΔ 2 ͭͷߦྻੵͷ࣮૷ͱͦͷ࣮ߦ࣌ؒΛൺֱ͢Δ (N = 1000) ૉ๿ͳߦྻੵͷ࣮૷ (3314 ms) for(int i=0; i<N; ++i) for(int j=0; j<N; ++j) { C[i*N+j] = 0; for(int k=0; k<N; ++k) C[i*N+j] += A[i*N+k]*B[k*N+j]; } B Λసஔͨ͠ߦྻੵͷ࣮૷ (2518 ms) for(int i=0; i<N; ++i) for(int j=0; j<N; ++j) { C[i*N+j] = 0; for(int k=0; k<N; ++k) C[i*N+j] += A[i*N+k]* B_trans[j*N+k]; } 62
66. ߴ଎Խͷྫ 3: εʔύʔεΧϥ ͋Δεςʔδ͕௕͍ϨΠςϯγΛ࣋ͭ৔߹ɺͦͷεςʔδΛෳ਺༻ҙ͢Δ͜ͱ͕ޮՌ తͰ͋Δɻ͜ͷΑ͏ͳઃܭΛεʔύεΧϥͱݺͿɻ Figure 47: εʔύʔεΧϥͷྫ [1] 63
67. σʔλϨϕϧฒྻੑ ௨ৗ͸ϓϩάϥϜ͸Ұͭͷ໋ྩʹରͯ͠ҰͭͷσʔλΛॲཧ͢Δࣄ͕ଟ͍ɻ͜ͷํࣜ ͸ MIMD ͱݺ͹ΕΔҰํͰɺՊֶٕज़ܭࢉ΍ը૾΍Ի੠ͳͲͷϚϧνϝσΟΞॲཧ Ͱ͸Ұͭͷ໋ྩΛෳ਺ͷσʔλʹରͯ͠ద༻͢Δ͜ͱ͕Α͋͘Δɻ͜ͷ৔߹ɺ໋̍ྩ Ͱෳ਺ͷॲཧ͕׬ྃ͢ΔͨΊɺޮ཰্͕ঢ͢Δɻ͜ͷΑ͏ʹॲཧͷର৅ͱͳΔσʔλ ͷ࣋ͭજࡏతͳฒྻੑΛσʔλϨϕϧฒྻੑͱݺͿɻ Ұൠͷϓϩηοα͸͜ͷσʔλϨϕϧฒྻੑΛੜ͔ͯ͠ɺϓϩάϥϜΛߴ଎Խ͢Δͨ Ίͷػߏ΍ઐ༻໋ྩΛ࣮૷͍ͯ͠ΔɻͦΕΒʹ͍ͭͯҎԼͷ̎ͭͷྫΛ঺հ͢Δɻ • ϚϧνϝσΟΞ SIMD • ϕΫτϧɾΞʔΩςΫνϟ • GPU 64
68. ߴ଎Խͷྫ 4: ϚϧνϝσΟΞ SIMD SIMD ͸Ի੠΍ը૾ͳͲͷϚϧνϝσΟΞσʔλͷܭࢉ͕ɺ8 Ϗοτ΍ 16 ϏοτͷΑ ͏ͳ୹͍σʔλ௕Λར༻͢Δ͜ͱ͔ΒɺͦΕΛޮ཰తʹܭࢉ͢ΔࢼΈ͔Βੜ·Εͨɻ ྫ͑͹ɺ32 ϏοτͷԋࢉػΛ 8 Ϗοτ × 4 ͭͷσʔλʹ෼͚ͯɺΩϟϦʔͷ఻೻Λ ్தͰࢭΊΔ͜ͱͰ̐ͭͷՃࢉΛಉ࣌ʹܭࢉͰ͖ΔɻҰൠతʹ͸ɺ128 Ϗοτ͔Β 512 Ϗοτͷԋࢉػ͓ΑͼϨδελ͕༻ҙ͞Ε͍ͯΔ͜ͱ͕ଟ͍ɻ SIMD Λ༻͍Δͱ௨ৗͷ໋ྩΛԿ౓΋࢖༻͢ΔΑΓ໋ྩ਺ΛݮΒ͢͜ͱ͕Ͱ͖Δɻ fld f5 , a # Load scalar a splat .4D f0 , f0 # Make 4 copies of a fld.4D f1 , 0(x5) # Load X[i] ... X[i+3] fmul .4D f1 , f1 , f0 # f1[0] = f1 [0]*a; ...; f1[3] = f1 [3]*a; fld.4D f2 , 0(x6) # Load Y[i] ... Y[i+3] fadd .4D f2 , f2 , f1 # f2[0] = f1 [0]+b; ...; f2[3] = f2 [3]+b; fsd.4D f2 , 0(x6) # Store Y[j] ... Y[j+3] 65
69. SIMD ͷσϝϦοτͱϕΫτϧΞʔΩςΫνϟ ϚϧνϝσΟΞ SIMD ʹ͸ҎԼͷܽ఺͕͋Δɻ • ֤ϕΫτϧ௕ຖʹ໋ྩ͕ଘࡏ͓ͯ͠Γɺ໋ྩͷ਺͕૿͑΍͍͢ • Ϊϟβʔ/εΩϟλʔͷΑ͏ͳΞυϨογϯάϞʔυ͕ແ͍ • ཁૉ͝ͱͷ৚݅෇͖࣮ߦΛαϙʔτ͍ͯ͠ͳ͍ ϕΫτϧΞʔΩςΫνϟ͸ SIMD ΑΓॊೈͰɺΑΓߴ౓ͳσʔλฒྻԋࢉΛѻ͑Δઃ ܭͰ͋Δɻ͜Ε͸ɺෳ਺ͷϨδελͱԋࢉϢχοτΛฒ΂ͯͦΕΒΛಈతʹ૊Έ߹Θ ͤΔ͜ͱͰɺ௕͍ύΠϓϥΠϯ΍ػೳϢχοτͷฒྻԽΛߦ͏ख๏Ͱ͋Δɻ ͜ΕʹΑΓ໋ྩΛ૿΍͢͜ͱແ͘ɺฒྻ໋ྩ࣮ߦ΍໋ྩͷΦʔόϔουͷܰݮ͕Ͱ͖ Δɻ·ͨɺΪϟβʔ/εΩϟλ΍৚݅෇͖ϕΫτϧԋࢉΛαϙʔτ͍ͯ͠Δ΋ͷ΋ ͋Δɻ 66
70. ϕΫτϧΞʔΩςΫνϟͷྫ Figure 48: ϕΫτϧΞʔΩςΫνϟͷྫ [6] 67
71. ߴ଎Խͷྫ 5: ϕΫτϧΞʔΩςΫνϟ ϕΫτϧΞʔΩςΫνϟ͸ҎԼͷߏ੒͔ΒͳΔɻ • ϕΫτϧϨδελ: ҰͭͷϕΫτϧΛอ࣋͢ΔɻRV64V Ͱ͸ 32 × 64bit ݸͷϨδελ͕͋ΔɻΫϩ εόʔεΠονΛ༻͍ͯϕΫλػೳϢχοτͷೖྗͱग़ྗʹ઀ଓ͞Ε͍ͯΔɻ • ϕΫλػೳϢχοτ: ෳ਺ͷ஋ʹରͯ͠ɺࢦఆͷϕΫτϧԋࢉΛ࣮ߦ͢ΔϢχοτɻͦΕͧΕ͕׬શʹ ύΠϓϥΠϯԽ͞Ε͓ͯΓɺຖαΠΫϧ৽͍͠ԋࢉΛ։࢝Ͱ͖Δɻ • ϕΫλϝϞϦϢχοτ: ϝϞϦ͔ΒϕΫλσʔλΛϩʔυɺετΞ͢ΔϢχοτɻ׬શʹύΠϓϥΠϯԽ ͞Ε͓ͯΓɺΦʔόϔουͷޙʹຖαΠΫϧ৽͍͠ϫʔυΛҠಈͰ͖Δɻ • εΧϥϨδελ: ϕΫλϝϞϦϢχοτͰར༻͢ΔΞυϨεΛܭࢉͨ͠ΓɺϕΫ λػೳϢχοτʹεΧϥ஋Λೖྗ͢ΔϢχοτɻ 68
72. ϕΫτϧΞʔΩςΫνϟͷΞηϯϒϦ (DAXPY) ϕΫτϧܭࢉͱͯ͠Α͘࢖ΘΕΔ໋ྩΛྫʹ RISC-V ͷϕΫτϧ໋ྩͷΞηϯϒϦࣔ ͢ɻ2 ͭͷϕΫτϧ X ͱ Y ͱ 1 ͭͷεΧϥ a Λ༻͍ͯɺҎԼΛܭࢉ͢Δɻ Y = a × X + Y vsetdcfg 4 * FP64 # Enable 4 DP FP vregs fld f0 , a # Load scalar a vld v0 , x5 # Load vector X vmul v1 , v0 , f0 # Vector scalar multiply vld v2 , x6 # Load vector Y vadd v3 , v1 , v2 # Vector -vector add vst v3 , x6 # Store the sum vdisable # Disable vector regs 69
73. ϕΫτϧΞʔΩςΫνϟͷಈతྫ Figure 49: ϕΫλػೳϢχοτͷฒྻԽͷྫ [6] 70
74. GPU ࠓճ·ͱΊ͖Ε·ͤΜͰͨ͠ɻεϛϚηϯɻ ɻ ୅ΘΓͱ͍͏ͷ͸͋ΕͰ͕͢ɺҎԼචऀͷϒϩάΛࢀর͍͚ͨͩΔͱخ͍͠Ͱ͢ɻ γϦʔζ෺Ͱେମ 7 هࣄ͘Β͍͋Γ·͢ɻ https://csam.hatenablog.com/entry/2019/02/04/154700 71
75. ίϯύΠϥʹΑΔܭࢉͷߴ଎Խ
76. ίϯύΠϥͱ͸ ߴڃݴޠͰॻ͔ΕͨϓϩάϥϜΛػցޠʹม׵͢Δ΋ͷͷ͜ͱͰ͋Δɻ͜Ε͸ɺ1950 ೥୅ʹॳΊͯ FORTRAN ͷΑ͏ͳϓϩάϥϜ͕։ൃ͞Εͨ࣌ʹ໊෇͚ΒΕͨɻ ҎԼʹ C ݴޠͷιʔείʔυΛػցޠʹม׵͢Δ༷ࢠΛࣔ͢ɻ Figure 50: C ݴޠͷίϯύΠϧ (http://gihyo.jp/dev/serial/01/c-programming-introduction/0001) 72
77. ίϯύΠϧํࣜͱΠϯλϓϦλํࣜ ίϯύΠϥʹ͸ɺೖྗͷϓϩάϥϜΛ׬શʹػցޠʹม׵ͯ͠ϑΝΠϧͱͯ͠ੜ੒͠ ͨޙʹͦΕΛ࣮ߦ͢ΔίϯύΠϧํࣜͱɺϑΝΠϧͷੜ੒ΛߦΘͣʹͦͷ··ղऍ͠ ͯೖྗͷϓϩάϥϜΛ࣮ߦ͢ΔΠϯλʔϓϦλํ͕ࣜ͋Δɻ • ίϯύΠϧํࣜ: CɺFORTRANɺRust • ΠϯλϓϦλܗࣜ: PythonɺRubyɺJavascript ·ͨɺೖྗͷϓϩάϥϜΛ࣮ࡍͷػցޠͰ͸ͳ͘Ծ૝తͳ໋ྩʹม׵͢Δ΋ͷ΋͋ Δɻ͜ΕΒ͸ͦͷԾ૝໋ྩΛղऍ͢ΔϓϩάϥϜΛιϑτ΢ΣΞͱͯ͠ಈ࡞ͤ͞Δ͜ ͱʹΑΓ࣮ߦΛߦ͏ɻ Ծ૝ػցΛ༻͍ͨϓϩάϥϛϯάݴޠͷྫʹ Jave ͕͋ΔɻJava ͸ίϯύΠϧํࣜͱ ΠϯλϓϦλํࣜͷ྆ํͰ࣮ߦ͕ՄೳͰ͋Δɻ 73
78. ίϯύΠϥͷॲཧͷྲྀΕ Figure 51: ίϯύΠϥͷॲཧͷྲྀΕ [10] 74
79. ίϯύΠϥʹΑΔ࠷దԽͷख๏ ໨తϓϩάϥϜͷ࠷దԽͱ͸ɺޮ཰ͷྑ͍໨తϓϩάϥϜʹ͢Δͱ͍͏͜ͱͰ͋Δɻ ࠷దԽʹ͸ϓϩάϥϜͷ࣮ߦ଎౓Λૣͨ͘͠ΓɺαΠζΛখ͘͢͞ΔͳͲ༷ʑͳछྨ ͕͋Δ͕ɺຊࢿྉͰ͸ߴ଎Խʹ͍ͭͯऔΓѻ͏͜ͱʹ͢Δɻ࣮ߦ଎౓ͷ޲্ʹ͸େ͖ ͘ 3 ͭͷ޻෉͕ߟ͑ΒΕΔ [11]ɻ • ໋ྩͷ࣮ߦճ਺ΛݮΒ͢ • ΑΓૣ໋͍ྩΛ࢖͏ • ฒྻ౓Λ্͛Δ ্هΛͲͷ༷ʹ૊Έ߹ΘͤΔ͔͸ɺ࣮ߦର৅ͷίϯϐϡʔλʹΑͬͯҟͳͬͯ͘Δɻ ྫ͑͹ɺಉ࣌ʹෳ਺ͷ໋ྩΛ࣮ߦͰ͖ΔεʔύεΧϥϚγϯʹ͓͍ͯ͸ 3 ͕༗ޮͰ͋ Δɻ·ͨɺCISC Ϛγϯͷ༷ʹෳ߹తͳ໋ྩΛଟ࣋ͭ͘ϓϩηοαͷ৔߹ʹ͸ɺͦΕ ΒΛ͏·͘࢖͏͜ͱͰ໋ྩͷ࣮ߦճ਺ΛݮΒ͢͜ͱ͕Ͱ͖Δɻ 75
80. ࠷దԽͷൣғ ϓϩάϥϜͷ͘͝Ұ෦ͷΈΛղੳͯ͠࠷దԽ͢Δ͜ͱΛہॴత࠷దԽɺͦͷશମʹ ౉ͬͯ࠷దԽ͢Δ͜ͱΛେҬత࠷దԽͱݺͿɻಛʹɺ໋֤ྩͷۙ͘͝ลͷΈΛݟͯߦ ΘΕΔ࠷దԽΛ peephole ࠷దԽͱݺͿɻ ࠷దԽ͸தؒදݱͱΞʔΩςΫνϟʹґଘͨ͠ػցޠͷ૒ํʹରͯ͠ߦΘΕΔɻ 76
81. ໋ྩͷ࣮ߦճ਺ΛݮΒ͢ ໋ྩͷ࣮ߦճ਺ΛݮΒ͢ʹ͸ҎԼͷํ๏͕͋Δ [11]ɻ 1. 1 ౓࣮ߦͨ݁͠ՌΛ࠶ར༻͢Δ: Common Subexpression Elimination ͳͲ 2. Մೳͳ΋ͷ͸ίϯύΠϧ࣌ʹ࣮ߦ͢Δ: Constant Folding ͳͲ 3. ໋ྩΛΑΓ࣮ߦස౓ͷ௿͍ͱ͜Ζ΁Ҡ͢: Code Motion ͳͲ 4. ࣮ߦճ਺ΛݮΒ͢Α͏ʹϓϩάϥϜͷܗΛม׵͢Δ: Loop Transformation ͳͲ 5. ࣜͷੑ࣭Λར༻࣮ͯ͠ߦΛม׵͢Δ: ୅਺ͱ࿦ཧࣜͷ׆༻ͳͲ 6. ৑௕ͳ໋ྩΛऔΓআ͘: Dead Code Elimination ͳͲ 7. ಛघԽ͢Δ: Function Inliningɺ൑ఆஔ͖׵͑ͳͲ ࠓճ͸্هͷҰ෦Λ঺հ͢Δɻ࣮͸্هͷز͔ͭͷຊ࣭తͳมܗख๏͸ಉ͡Ͱ͋Δɻ ྫ͑͹ɺCommon Subexpression Elimination ͱ Code Motion ͸ Partial Subexpression Elimination ͷҰछͰ͋Δɻ 77
82. Common Subexpression Elimination ҎԼͷΑ͏ʹจͷதʹڞ௨ͷؚ͕ࣜ·ΕΔ৔߹ɺͦΕΒΛҰ౓͚ͩܭࢉ͠Ұ࣌ม਺ͱ ͯ֬͠อͨ͠ޙɺͦΕΛ࢖͍ճ͢Α͏ʹϓϩάϥϜΛมߋ͢Δɻ c = a + b // (1) ... e = (a + b) * d // (2) t = a + b // Ұ࣌ม਺ͱͯ͠ܭࢉ c = t // Ұ࣌ม਺Λར༻1 ... e = t * d // Ұ࣌ม਺Λར༻2 ڞ௨෦෼ࣜͷ࡟আʹ͸ҰൠʹҎԼͷ 3 ͭͷ৚͕݅ඞཁͰ͋Δɻ i. (1) ͱ (2) ͷ a+b ͸ಉ͡ܗͷࣜͰ͋Δ ii. (2) ͷܭࢉͷલʹඞͣ (1) ͷܭࢉ͕ͳ͞Ε͍ͯΔ iii. (1) ͱ (2) ͷؒͰɺa ͱ b ͷ஋͕มΘΒͳ͍ 78
83. Constant Folding ఆ਺ͷܭࢉΛίϯύΠϧ࣌ʹߦ͏࠷దԽͷ͜ͱɻྫ͑͹ɺҎԼͷΑ͏ͳϓϩάϥϜʹ ରͯ͠ɺ a = 1.0 + 2.0 b = a * 3.0 d = c - b // ͸ม਺c a Λࣄલʹܭࢉͯ͠ఆ਺ͱ͠ɺͦͷ݁ՌΛ༻͍ͯ b ΋ܭࢉ͢Δͱɺ࠷ऴతʹҎԼͷΑ ͏ʹͳΔɻ d = c - 6.0 79
84. Loop Motion ϧʔϓͷ಺෦Ͱ஋͕มԽ͠ͳ͍ཁૉΛϧʔϓෆม (Loop Invariant) ͱݺͿɻLoop Motion ͸ϧʔϓෆมͳܭࢉΛ֎ʹͩ͢͜ͱͰɺܭࢉճ਺ΛݮΒ͢࠷దԽͰ͋ΔɻҎ Լʹ Loop Motion ͷ؆୯ͳྫΛࣔ͢ɻ for i = 1, n ... a = b * c d = i * 2 ... end a = b * c for i = 1, n ... d = i * 2 ... end Loop Motion ʹΑͬͯ࡟ݮ͞ΕΔܭࢉྔ͸ҰൠʹͦͷϧʔϓͷΠςϨʔγϣϯ਺ͱԋ ࢉࢠͷڧ౓ʹґଘ͢Δɻ্هͰ͸ n-1 ճͷ৐ࢉ͕࡟ݮ͞ΕΔɻ 80
85. ΑΓ଎໋͍ྩͷར༻ 1. هԱ֊૚Λ׆༻͢Δ: Register Allocation, Tiling ͳͲ 2. ϓϩηοαͷ࣋ͭߴ଎ͳ໋ྩΛ׆༻͢Δ: SIMD, incremental condition ͳͲ 3. ࿦ཧ΍਺ཧత৘ใΛ༻͍ͯΑΓ୯७ͳ໋ྩʹஔ͖׵͑Δ 81
86. Register Allocation Ϩδελ͸ίϯϐϡʔλͷ࣋ͭ࠷΋ߴ଎ͳهԱૉࢠͰ͋ΔͨΊɺͳΔ΂͘ϨδελΛ ࢖͏Α͏ʹϓϩάϥϜΛม׵͢Δͱߴ଎ʹͳΔɻ͔͠͠ɺϨδελͷ਺͸গͳ͍ͨ Ίɺม਺ʹରͯ͠ޮ཰తʹͦΕΒΛׂΓ౰ͯΔඞཁ͕͋Δɻ͜Ε͸ Register Allocation ͱݺ͹ΕΔɻ 82
87. Loop Tiling هԱ֊૚Λ׆͔ͨ͠ߴ଎Խͷ༗໊ͳྫʹ Loop Tiling ͕͋Δɻ͜Ε͸େ͖ͳ഑ྻΛෳ ਺ͷখ͞ͳ഑ྻʹ෼ղͯ͠ɺͦͷൣғͰܭࢉΛߦ͏͜ͱͰΞΫηεͷہॴੑΛߴΊΔ ࠷దԽͰ͋ΔɻҎԼʹ n ࣍ਖ਼ํߦྻͷߦྻੵͷྫΛࣔ͢ɻ for i = 1, n for j = 1, n for k = 1, n C(i,j) += A(i,k) * B(j,k) end // k end // j end // i 83
88. loop tiling Λద༻ͨ͠ߦྻੵͷྫ for ti = 1, n, t for tj = 1, n, t for tk = 1, n, t for i = ti , n, min(ti + t, n) for j = tj , n, min(tj + t, n) for k = tk , n, min(tk + t, n) C(i,j) += A(i,k) * B(j,k) end // k end // j end // i end // tk end // tj end // ti 84
89. σόΠεͷ࣋ͭߴ଎ͳ໋ྩΛ׆༻͢Δ ଟ͘ͷσόΠε͸Ұൠతͳ໋ྩͷଞʹɺ͋Δಛघͳಈ࡞ʹରͯ͠ߴ଎ʹಈ࡞͢Δ໋ྩ Λ౥ࡌ͍ͯ͠Δ͜ͱ͕͋Δɻྫ͑͹ɺϨδελ಺ͷ஋ʹରͯ̍͠ΛՃࢉ͠ɺͦͷ݁Ռ Λ༻͍ͯ෼ذ໋ྩΛ࣮ߦ͢Δ໋ྩ౳Ͱ͋Δɻ͜Ε͸ϧʔϓͷऴྃ൑ఆͳͲʹΑ͘ར༻ ͞ΕΔɻ·ͨɺۙ೥ͷσόΠεʹΑ͘౥ࡌ͞Ε͍ͯΔϕΫτϧԋࢉػΛ׆༻͢Δ໋ྩ ΋Α͘ར༻͞ΕΔɻRISC-V ຊͷϕΫτϧ໋ྩΛࢀরɻ͋ͱͰॻ͘ 85
90. σόΠεͷ࣋ͭߴ଎ͳ໋ྩΛ׆༻͢Δ CPU ͷϕΫλϓϩηοα΍ SIMD ͷ߲໨Ͱઆ໌ͨ͠ͷͰɺͦͪΒΛࢀরɻ 86
91. ฒྻ౓Λ্͛Δ ίϯύΠϥʹΑΔϓϩάϥϜͷ࠷దԽͷҰͭʹɺϓϩάϥϜΛฒྻʹ࣮ߦ͢Δ͜ͱʹ Αͬͯߴ଎Խ͢Δख๏͕͋Δɻ͜Ε͸ಛʹɺݱ୅ͷ CPU ͷ༷ʹେن໛ͳσόΠεͰ ಛʹ༗ޮͳख๏Ͱ͋ΓɺҰൠͷϓϩάϥϜͰ΋සൟʹར༻͞Ε͍ͯΔɻ ฒྻ౓Λ্͛Δख๏͸ɺେ͖͘ҎԼͷ̎ͭʹ෼ྨ͢Δ͜ͱ͕Ͱ͖Δɻ 1. ໋ྩϨϕϧͷฒྻ࣮ߦ 2. ϓϩηοαͷϨϕϧͷฒྻ࣮ߦ 87
92. ໋ྩϨϕϧͷฒྻ࣮ߦ CPU ͷষͰۙ୅తͳϓϩηοα͸ԋࢉϢχοτΛෳ਺΋͓ͬͯΓɺ໋ྩΛฒྻʹ࣮ ߦ͢Δ͜ͱ͕Ͱ͖Δ͜ͱΛड़΂ͨɻ͜͜Ͱ͸ίϯύΠϥ͔Βੜ੒͢ΔίʔυΛ޻෉͢ Δ͜ͱͰෳ਺ͷԋࢉػ্Ͱ࣮ߦ͞ΕΔ໋ྩ਺Λ૿΍͠ɺϓϩάϥϜΛߴ଎Խ͢Δख๏ Λઆ໌͢Δɻ ίϯύΠϥʹΑΓɺ໋ྩ࣮ߦͷޮ཰Λ্͛ΔΑ͏ʹͦͷॱংΛมߋ͢Δ͜ͱΛ໋ྩε έδϡʔϦϯάͱݺͿɻಛʹϧʔϓʹରͯ͠ɺ໋ྩεέδϡʔϦϯάΛߦ͏͜ͱΛι ϑτ΢ΣΞύΠϓϥΠχϯάΛݺͿɻ͜ͷख๏͸ओʹεʔύʔεΧϥϓϩηοα΍ VLIW ͷΑ͏ͳߏ੒Λ΋ͭϓϩηοαʹରͯ͠༗ޮͰ͋Δɻ 88
93. ιϑτ΢ΣΞύΠϓϥΠχϯά ͜͜Ͱ͸ҎԼͷ৚݅Λຬͨ͢ϓϩηοαΛ࢖༻ͨ͠ࡍͷιϑτ΢ΣΞύΠϓϥΠχϯ άͷྫΛࣔ͢ɻ • 1 ͭͷԋࢉ໋ྩͱ̍ͭͷϝϞϦૢ࡞໋ྩΛಉ࣌ʹ࣮ߦͰ͖Δɻ • ԋࢉ໋ྩ͸̍αΠΫϧͰ׬ྃ͢Δ • ϝϞϦૢ࡞໋ྩͷϩʔυ໋ྩ͸ 2 αΠΫϧɺετΞ໋ྩ͸ 1 αΠΫϧͰ׬ྃ͢Δ ҎԼʹɺϧʔϓ಺෦Ͱ୯७ͳܭࢉΛߦ͏ϓϩάϥϜͱͦͷΞηϯϒϦΛࣔ͢ɻ࣍ʹ͜ ͷϓϩάϥϜͷ֤ΠςϨʔγϣϯʹ͓͚Δ໋ྩ࣮ߦͷਪҠΛࣔ͢ɻ for i = 1, n A[i] = A[i] * b + c end 1: Load r1 A[i] 2: Mul r4 r1 r2 // b in r2 3: Add r5 r4 r3 // c in r3 4: Store A[i] r5 5: loop_check 89
94. ιϑτ΢ΣΞύΠϓϥΠχϯάͳ͠ͷ৔߹ͷΠςϨʔγϣϯͷ༷ࢠ 1 i = 1 i = 2 i = 3 i = 4 2 Load r1 A[1] 3 Mul r4 r1 r2 4 Add r5 r4 r3 5 Store A[i] r5 6 7 loop check 8 Load r1 A[2] 9 Mul r4 r1 r2 10 Add r5 r4 r3 11 Store A[2] 12 13 loop check 90
95. ιϑτ΢ΣΞύΠϓϥΠχϯάΛద༻ͨ͠ϓϩάϥϜͱΞηϯϒϦ for i = 1, n, 4 A[i] = A[i] * b + c A[i+1] = A[i+1] * b + c A[i+2] = A[i+2] * b + c A[i+3] = A[i+3] * b + c end 1: Load r1 A[i] 2: Mul r4 r1 r2 3: Add r5 r4 r3 4: Store A[i] r5 5: Load r1 A[i+1] 6: Mul r4 r1 r2 7: Add r5 r4 r3 8: Store A[i+1] r5 9: Load r1 A[i+2] 10: Mul r4 r1 r2 11: Add r5 r4 r3 12: Store A[i+2] r5 13: Load r1 A[i+3] 14: Mul r4 r1 r2 15: Add r5 r4 r3 16: Store A[i+3] r5 17: loop_check 91
96. ιϑτ΢ΣΞύΠϓϥΠχϯά͋Γͷ৔߹ͷΠςϨʔγϣϯͷ༷ࢠ 1 i = 1 i = 2 i = 3 i = 4 2 Load r1 A[1] 3 Mul r4 r1 r2 4 Add r5 r4 r3 Load r1 A[2] 5 6 Store A[1] r5 Mul r4 r1 r2 7 Add r5 r4 r3 Load r1 A[3] 8 9 Store A[2] r5 Mul r4 r1 r2 10 Add r5 r4 r3 Load r1 A[4] 11 12 Store A[3] r5 Mul r4 r1 r2 13 Add r5 r4 r3 92
97. Initiation Interval લεϥΠυͷද͔Βɺϧʔϓͷ్த͔Β 3 αΠΫϧͰ࣍ͷΠςϨʔγϣϯΛ։࢝ͯ͠ ͍Δ͜ͱ͕෼͔Δ (ྫ: 6 9 αΠΫϧ໨)ɻ͜ͷִؒΛ Initiation Interval(II) ͱݺͿɻॳ Ίͷૉ๿ͳϧʔϓͷ II ͸ 6 Ͱ͋ͬͨ͜ͱ͔Β΋෼͔ΔΑ͏ʹɺII ͕খ͍͞΄Ͳϧʔϓ ʹඞཁͳΫϩοΫαΠΫϧ਺͸ݮগ͢Δɻ Ҏ্͔Βɺιϑτ΢ΣΞύΠϓϥΠχϯάΛߦ͏ͱૉ๿ͳ৔߹ΑΓߴ଎ʹϓϩάϥϜ Λ࣮ߦͰ͖Δ͜ͱ͕Θ͔ͬͨɻ͜Ε͸֤ϧʔϓؒʹ͓͚Δऴྃ൑ఆΛݮΒ͢͜ͱͰɺ ϓϩηοαͷύΠϓϥΠϯΛखಈͰຒΊΔΑ͏ʹϓϩάϥϜΛ૊Ή͜ͱʹΑΓੜͯ͡ ͍Δɻݱ୅ͷϓϩηοαͷଟ͕͘౤ػ࣮ߦʹΑͬͯऴྃ൑ఆΛ଴ͨͣʹ࣍ͷ໋ྩΛ ϑΣον͠ɺฒྻʹ࣮ߦ͍ͯ͠ΔͨΊɺૉ๿ͳϧʔϓͷྫͷΑ͏ʹඇޮ཰ʹ࣮ߦ͕ͳ ͞ΕΔ͜ͱ͸গͳ͍ɻ͔͠͠ɺ͜ΕΒ͸෼ذ༧ଌͷਫ਼౓΍෼ذͷ֬཰ʹґଘ͢Δͨ Ίɺϋʔυ΢ΣΞ͕ਐԽͨ͠ݱࡏͰ΋ґવͱͯ͠ιϑτ΢ΣΞʹΑΔ໋ྩεέδϡʔ Ϧϯά͸༗ޮͰ͋Δɻ 93
98. ϓϩηοαϨϕϧͷฒྻ࣮ߦ CPU ͷষͰۙ୅తͳϓϩηοα͸ෳ਺ͷಉҰͳܭࢉϢχοτΛ಺෦ʹ͓࣋ͬͯΓ (Ϛ ϧνίΞ)ɺ໋ྩΛฒྻʹ࣮ߦ͢Δ͜ͱ͕Ͱ͖Δ͜ͱΛड़΂ͨɻ͜͜Ͱ͸ॲཧΛෳ੡ ͠ɺෳ਺ͷϢχοτͷ্Ͱฒྻʹ࣮ߦΛߦ͏͜ͱͰϓϩάϥϜΛߴ଎Խ͢Δख๏Λ঺ հ͢ΔɻͲͷ༷ʹҰͭͷॲཧΛ෼ׂ࣮͠ߦ͢Δ͔Ͱɺ2 ͭͷύλʔϯ͕͋Δɻ 1. σʔλฒྻ 2. λεΫฒྻ ·ͨɺฒྻϚγϯ͸ओهԱͷ઀ଓͷ͔ͨ͠ʹΑͬͯ 2 छྨʹ෼ྨͰ͖Δɻ 1. ڞ༗ϝϞϦํࣜ 2. ෼ࢄϝϞϦํࣜ 94
99. ·ͱΊ • ίϯϐϡʔλ͸਺ͷදݱʹ 2 ਐ਺Λ༻͍͓ͯΓɺεΠονճ࿏Λݩʹߏ੒͞Εͨ ࿦ཧճ࿏ͰϒʔϧԋࢉΛߦ͍ͬͯΔ • Ұൠతͳίϯϐϡʔλ͸ϊΠϚϯܕϚγϯͱݺ͹ΕΔߏ੒ͱͳ͍ͬͯΔ • CPU ͸ ISA ʹΑΓ໋ྩΛ࣮ߦ͠ɺͦΕΒ͸ڞ௨ͷ࿦ཧճ࿏Λ༻͍࣮ͯߦ͞ΕΔ • CPU ͸ߴ଎ԽͷͨΊʹύΠϓϥΠϯԽ͞Ε͍ͯΔ͕ɺϋβʔυʹΑΓͦͷޮ཰ ͕མͪΔ͜ͱ͕͋Δ • ଎౓ͱ༰ྔͷҟͳΔϝϞϦΛ֊૚Խ͢Δ͜ͱͰϝΠϯϝϞϦ΁ͷΞΫηε࣌ؒΛ ؇࿨͍ͯ͠Δ • CPU ͸ಉ͡ॲཧΛෳ਺ͷσʔλʹର࣮ͯ͠ߦͰ͖Δ • ίϯύΠϥ͸ CPU ͷੑ࣭Λར༻ͯ͠ϓϩάϥϜΛߴ଎Խ͍ͯ͠Δ ߴ଎Խͷଟ͘͸ίϯϐϡʔλͷΞʔΩςΫνϟࣗମͷੑ࣭Λ͏·͘ར༻͢Δ͜ͱͰୡ ੒Ͱ͖Δέʔε͕ଟ͍ɻैͬͯɺجૅతͳίϯϐϡʔλͷ஌ࣝΛ͖ͪΜͱཧղ͢Δ͜ ͱ͕ॏཁͰ͋Δɻ 95
100. ײ૝ͱࠓޙͷల๬ ࠓճͷൃදͰ͸ࣄલʹܭը͍ͯͨ͠ GPU ͷ෦෼Λ׬શʹൈ͍ͯ͠·͍ɺਃ͠༁͋Γ ·ͤΜͰͨ͠ɻ ɻ·ͨɺൃද࣌ؒ΍ࢿྉͷ࡞੒४උ࣌ؒͷ໰୊Ͱ CPU ΍ίϯύΠϥʹ ͍ͭͯ΋ਂ͍ೖΓͰ͖ͳ͔ͬͨͷͰɺͲ͔͜Ͱͦͷล΋ൃද͍ͨ͠ɻ ൓লΛ౿·͑ͯࠓޙͷൃද͍ͨ͠಺༰͸ҎԼɻ • CPU: εʔύεΧϥͷৄࡉɺOoO ࣮ߦɺεϨουϨϕϧฒྻੑɺ෼ذ༧ଌɺη ΩϡϦςΟ໰୊ • GPU: جૅɺGPU ϓϩάϥϛϯάͷྫ • ίϯύΠϥ: ϑϩϯτΤϯυɺσʔλϑϩʔղੳͳͲͷղੳܥ • ͦͷଞ: FPGA, Domain Specific Architecture ಡྃɺ͓ർΕ༷Ͱͨ͠ɻ 96
101. ࠷ޙʹ ؒҧ͍΍͝ҙݟ͋Ε͹ͥͻڭ͍͑ͯͩ͘͞ɻҎԼɺ࿈བྷઌͰ͢ɻ Twitter ID @tkclimb0911 97
102. References
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