Sdpnal+　is　a　MATLAB　software　package　that　implements　an　augmented　Lagrangian　based　method　to　solve　large　scale　semidefinite　programming　problems　with　bound　constraints.　The　implementation　was　initially　based　on　a　majorized　semismooth　Newton-CG　augmented　Lagrangian　method,　here　we　designed　it　within　an　inexact　symmetric　Gauss-Seidel　based　semi-proximal　ADMM/ALM　(alternating　direction　method　of　multipliers/augmented　Lagrangian　method)　framework　for　the　purpose　of　deriving　simpler　stopping　conditions　and　closing　the　gap　between　the　practical　implementation　of　the　algorithm　and　the　theoretical　algorithm.　The　basic　code　is　written　in　MATLAB,　but　some　subroutines　in　C　language　are　incorporated　via　Mex　files.　We　also　design　a　convenient　interface　for　users　to　input　their　SDP　models　into　the　solver.　Numerous　problems　arising　from　combinatorial　optimization　and　binary　integer　quadratic　programming　problems　have　been　tested　to　evaluate　the　performance　of　the　solver.　Extensive　numerical　experiments　conducted　in　[L.Q.　Yang,　D.F.　Sun,　and　K.C.　Toh,　SDPNAL+:　A　majorized　semismooth　Newton-CG　augmented　Lagrangian　method　for　semidefinite　programming　with　nonnegative　constraints,　Math.　Program.　Comput.　7　(2015),　pp.　331-366]　show　that　the　proposed　method　is　quite　efficient　and　robust,　in　that　it　is　able　to　solve　98.9%　of　the　745　test　instances　of　SDP　problems　arising　from　various　applications　to　the　accuracy　of　in　the　relative　KKT　residual.