diff --git a/examples/gk/gk.jl b/examples/gk/gk.jl
index 0ca23c63d97dd..9fe1d50b606ae 100644
--- a/examples/gk/gk.jl
+++ b/examples/gk/gk.jl
@@ -8,7 +8,7 @@
 function myunifskew(n)
     A = zeros(n, n)
 
-    print("A[i,j] initialized with zeros \n");
+    #print("A[i,j] initialized with zeros \n");
 
     for i=1:n
     	for j=1:i-1
@@ -28,8 +28,8 @@ function myunifskew(n)
             
     	end
     	if rem(i,1000) == 0
-	    print(i)
-	    print("\n")
+	    #print(i)
+	    #print("\n")
     	end
     end
 
@@ -64,15 +64,15 @@ function gk(n, myeps)
 	
 	csum = zeros(n)
 
-	tic()
+#	tic()
 	while(stop != 1)
      	    t=t+1
      	    iter=t
      	    #iteration number
   	    
      	    if rem(iter, 100) == 0
-         	print(iter)
-		print("\n")
+         	#print(iter)
+		#print("\n")
      	    end
 	    
 	    for i=1:n
@@ -114,7 +114,7 @@ function gk(n, myeps)
 
 	end
 	    
-	times[KK] = toc()
+	times[KK] = 0#toc()
 	iteration[KK] = iter
 	    
 	x = X/t
@@ -133,7 +133,7 @@ function gk(n, myeps)
     	end
 	    
 	if Axepse==n
-	    print(" \n Ax <= eps*e  \n")
+	    #print(" \n Ax <= eps*e  \n")
 	end
 
 	#if A*x <= eps*e
@@ -148,38 +148,38 @@ function gk(n, myeps)
 	end
 	    
         if errorlmt ==n
-    	    print("Assertion condition is satisfied i.e. AX-U<10^-8 \n")
+    	    #print("Assertion condition is satisfied i.e. AX-U<10^-8 \n")
 	else
     	    print("Error:  AX-U<10^-8 not satisfied \n")
 	end
 
 	#print("welcome")	
 	    
-	print("Time for \n")
-	print(eps)
-	print("\n")
-	print("is  \n")
-	print(times[KK])
-	print("\n")
-	print("Number of iteration is \n")
-	print(iteration[KK])
-	print("\n")
+	# print("Time for \n")
+	# print(eps)
+	# print("\n")
+	# print("is  \n")
+	# print(times[KK])
+	# print("\n")
+	# print("Number of iteration is \n")
+	# print(iteration[KK])
+	# print("\n")
     end
 
-    print("Epsilon vector is \n")
-    print(myeps)
-    print("\n")
-    print("time vector is \n ")
-    print(times)
-    print("\n")
-    print("Iteration Vector is \n")
-    print(iteration)
-    print("\n \n")
+    # print("Epsilon vector is \n")
+    # print(myeps)
+    # print("\n")
+    # print("time vector is \n ")
+    # print(times)
+    # print("\n")
+    # print("Iteration Vector is \n")
+    # print(iteration)
+    # print("\n \n")
 
     #out = [myeps, time, iteration]
     #print("Epsilon-Time-Iteration tradeoff \n")
     #print(out)
-    print("\n \n")
+    #print("\n \n")
 
 end
 #end # @profile begin
diff --git a/examples/stockcorr.jl b/examples/stockcorr.jl
index 58ba80a51b375..5647f6c4dffbe 100644
--- a/examples/stockcorr.jl
+++ b/examples/stockcorr.jl
@@ -7,7 +7,7 @@ function stockcorr()
     CurrentPrice = [78. 102.]     # Initial Prices of the two stocks
     Corr = [1. 0.4; 0.4 1.]       # Correlation Matrix
     T = 500                       # Number of days to simulate = 2years = 500days
-    n = 100000                    # Number of simulations
+    n = 3000                      # Number of simulations
     dt = 1/250                    # Time step (1year = 250days)
     Div=[0.01 0.01]               # Dividend
     Vol=[0.2 0.3]                 # Volatility
diff --git a/test/perf2/bounds.jl b/test/perf2/bounds.jl
deleted file mode 100644
index 1e26bf577f4c4..0000000000000
--- a/test/perf2/bounds.jl
+++ /dev/null
@@ -1,25 +0,0 @@
-function laplacian2(A::Matrix, B::Matrix)
-    d1 = 1
-    d2 = size(A,1)
-    for j = 2:size(B,2)-1
-        offset = (j-1)*size(A,1)
-        for i = 2:size(B,1)-1
-            ii = offset+i
-            B[ii] = A[ii+d1] + A[ii-d1] + A[ii+d2] + A[ii-d2] - 4*A[ii]
-        end
-    end
-end
-
-function time_laplacian(A::Matrix, niter::Int)
-    B = similar(A)
-    print("Laplacian of a matrix: ")
-    @time begin
-        for n = 1:niter
-            laplacian2(A, B)
-        end
-    end
-end
-
-time_laplacian(randn(1000,1000), 100)
-
-# Note: run time_laplacian with bounds-checking on and off (turn off by commenting out the middle two lines in cgutils.cpp's "emit_bounds_check"
diff --git a/test/perf2/fusion.jl b/test/perf2/fusion.jl
deleted file mode 100644
index 06fc26b8dc275..0000000000000
--- a/test/perf2/fusion.jl
+++ /dev/null
@@ -1,29 +0,0 @@
-function arith_vectorized(b,c,d)
-    a = b.*c + d + 1.0
-end
-
-function arith_loop(b,c,d)
-    a = similar(b)
-    for i = 1:length(b)
-        a[i] = b[i]*c[i] + d[i] + 1.0
-    end
-end
-
-function compare_arith(len::Int, niter::Int)
-    b = randn(len)
-    c = randn(len)
-    d = randn(len)
-    print("With vectorized: ")
-    @time begin
-        for n in 1:niter
-            a = arith_vectorized(b,c,d)
-        end
-    end
-    print("With loop: ")
-    @time begin
-        for n in 1:niter
-            a = arith_loop(b,c,d)
-        end
-    end
-end
-compare_arith(1_000_000, 10)
diff --git a/test/perf2/inline.jl b/test/perf2/inline.jl
deleted file mode 100644
index d7820c2b8656a..0000000000000
--- a/test/perf2/inline.jl
+++ /dev/null
@@ -1,36 +0,0 @@
-function cmp_with_less(x::Vector)
-    count::Int = 0
-    for i = 1:length(x)-1
-        if x[i] < x[i+1]
-            count += 1
-        end
-    end
-    return count
-end
-
-function cmp_with_func(x::Vector, f::Function)
-    count::Int = 0
-    for i = 1:length(x)-1
-        if f(x[i], x[i+1])
-            count += 1
-        end
-    end
-    return count
-end
-
-function compare_inline(len::Int, niter::Int)
-    x = randn(len)
-    print("With inline less: ")
-    @time begin
-        for n in 1:niter
-            count = cmp_with_less(x)
-        end
-    end
-    print("With function less: ")
-    @time begin
-        for n in 1:niter
-            count = cmp_with_func(x, isless)
-        end
-    end
-end
-compare_inline(1_000_000, 10)
diff --git a/test/perf2/looping.jl b/test/perf2/looping.jl
deleted file mode 100644
index cab42a70c9e86..0000000000000
--- a/test/perf2/looping.jl
+++ /dev/null
@@ -1,40 +0,0 @@
-function testlooping1(p, sz)
-    index = 1        
-    for k = 1:sz[3]
-        for j = 1:sz[2]
-            jkprod = j*k
-            for i = 1:sz[1]
-                p[index] = i*jkprod
-                index += 1
-            end
-        end
-    end
-end
-
-function oneloop(p, index::Int, jkprod::Int, imax::Int)
-    for i = 1:imax
-        p[index] = i*jkprod
-        index += 1
-    end
-    return index
-end
-
-function testlooping2(p, sz)
-    index = 1        
-    for k = 1:sz[3]
-        for j = 1:sz[2]
-            index = oneloop(p, index, j*k, sz[1])
-        end
-    end
-end
-
-sz = (1000,1000,10)
-p = Array(Int, sz)
-szsmall = (10,10,10)
-psmall = Array(Int, szsmall)
-testlooping1(psmall, szsmall)
-@time testlooping1(p, sz)
-p1 = copy(p)
-testlooping2(psmall, szsmall)
-@time testlooping2(p, sz)
-@assert p == p1
diff --git a/test/perf2/perf2.jl b/test/perf2/perf2.jl
new file mode 100644
index 0000000000000..b485707ebe40c
--- /dev/null
+++ b/test/perf2/perf2.jl
@@ -0,0 +1,72 @@
+macro timeit(ex,name)
+    quote
+        t = Inf
+        for i=1:5
+            t = min(t, @elapsed $ex)
+        end
+        println($name, "\t", t*1000)
+    end
+end
+
+srand(1776)  # get more consistent times
+
+require("$JULIA_HOME/../../examples/list.jl")
+
+function listn1n2(n1::Int64,n2::Int64)
+    l1 = Nil{Int64}()
+    for i=n2:-1:n1
+        l1 = Cons{Int64}(i,l1)
+    end
+    l1
+end
+
+@timeit listn1n2(1,10^6) "cons    "
+gc()
+
+
+# issue #950
+load("$JULIA_HOME/../../examples/gk/gk.jl")
+@timeit gk(350,[0.1]) "gk      "
+
+# issue #942
+require("linalg_sparse.jl")
+s = sparse(ones(280,280));
+@timeit s*s "sparsemul"
+
+# issue #939
+y = [500000:-1:1];
+@timeit sortperm(y) "sortperm"
+
+# issue #938
+x = 1:600000;
+@timeit sparse(x,x,x) "sparserang"
+
+# issue #445
+load("$JULIA_HOME/../../examples/stockcorr.jl")
+@timeit stockcorr() "stockcorr"
+
+
+function cmp_with_func(x::Vector, f::Function)
+    count::Int = 0
+    for i = 1:length(x)-1
+        if f(x[i], x[i+1])
+            count += 1
+        end
+    end
+    return count
+end
+
+x = randn(200_000)
+@timeit (for n in 1:10; count = cmp_with_func(x, isless) end) "funarg  "
+
+
+function arith_vectorized(b,c,d)
+    a = b.*c + d + 1.0
+end
+
+len = 1_000_000
+b = randn(len)
+c = randn(len)
+d = randn(len)
+
+@timeit (for n in 1:10; a = arith_vectorized(b,c,d); end) "vectoriz"