Update Mojo examples (modular#817)

Changes as of modularml/modular@132eea1

Co-authored-by: modularbot <[email protected]>

examples/mandelbrot.mojo

@@ -35,7 +35,7 @@ fn mandelbrot_kernel_SIMD[
     for i in range(MAX_ITERS):
         if not t.reduce_or():
             break
-        y2 = y*y
+        y2 = y * y
         y = x.fma(y + y, cy)
         t = x.fma(x, y2) <= 4
         x = x.fma(x, cx - y2)
@@ -80,7 +80,9 @@ fn main():
         fn bench_parallel[simd_width: Int]():
             parallelize[worker](rt, height, height)
 
-        let parallelized_ms = Benchmark().run[bench_parallel[simd_width]]() / 1e6
+        let parallelized_ms = Benchmark().run[
+            bench_parallel[simd_width]
+        ]() / 1e6
         print("Parallelized:", parallelized_ms, "ms")
         print("Parallel speedup:", vectorized_ms / parallelized_ms)
 

examples/nbody.mojo

@@ -15,7 +15,6 @@
 # https://benchmarksgame-team.pages.debian.net/benchmarksgame/performance/nbody.html
 
 from utils.index import StaticTuple
-from algorithm import unroll
 from math import sqrt
 from benchmark import Benchmark
 
@@ -48,8 +47,8 @@ alias NUM_BODIES = 5
 fn offset_momentum(inout bodies: StaticTuple[NUM_BODIES, Planet]):
     var p = SIMD[DType.float64, 4]()
 
-    @parameter
-    fn _iter[i: Int]():
+    @unroll
+    for i in range(NUM_BODIES):
         p += bodies[i].velocity * bodies[i].mass
 
     var body = bodies[0]
@@ -59,10 +58,9 @@ fn offset_momentum(inout bodies: StaticTuple[NUM_BODIES, Planet]):
 
 
 fn advance(inout bodies: StaticTuple[NUM_BODIES, Planet], dt: Float64):
-    @parameter
-    fn _outer[i: Int]():
-        @parameter
-        fn _inner[j: Int]():
+    @unroll
+    for i in range(NUM_BODIES):
+        for j in range(NUM_BODIES - i - 1):
             var body_i = bodies[i]
             var body_j = bodies[j + i + 1]
             let diff = body_i.pos - body_j.pos
@@ -75,42 +73,31 @@ fn advance(inout bodies: StaticTuple[NUM_BODIES, Planet], dt: Float64):
             bodies[i] = body_i
             bodies[j + i + 1] = body_j
 
-        unroll[NUM_BODIES - i - 1, _inner]()
-
-    unroll[NUM_BODIES, _outer]()
-
-    @parameter
-    fn _update[i: Int]():
+    @unroll
+    for i in range(NUM_BODIES):
         var body = bodies[i]
         body.pos += dt * body.velocity
         bodies[i] = body
 
-    unroll[NUM_BODIES, _update]()
-
 
 fn energy(bodies: StaticTuple[NUM_BODIES, Planet]) -> Float64:
     var e: Float64 = 0
 
-    @parameter
-    fn _outer[i: Int]():
+    @unroll
+    for i in range(NUM_BODIES):
         let body_i = bodies[i]
         e += (
             0.5
             * body_i.mass
             * ((body_i.velocity * body_i.velocity).reduce_add())
         )
 
-        @parameter
-        fn _inner[j: Int]():
+        for j in range(NUM_BODIES - i - 1):
             let body_j = bodies[j + i + 1]
             let diff = body_i.pos - body_j.pos
             let distance = sqrt((diff * diff).reduce_add())
             e -= (body_i.mass * body_j.mass) / distance
 
-        unroll[NUM_BODIES - i - 1, _inner]()
-
-    unroll[NUM_BODIES, _outer]()
-
     return e
 
 
@@ -189,15 +176,12 @@ fn run():
     )
     offset_momentum(system)
 
-    let derived_energy: String = "Energy of System: "
-    print(derived_energy)
-    print(energy(system))
+    print("Energy of System:", energy(system))
 
     for i in range(50_000_000):
         advance(system, 0.01)
 
-    print(derived_energy)
-    print(energy(system))
+    print("Energy of System:", energy(system))
 
 
 fn benchmark():

examples/notebooks/BoolMLIR.ipynb

@@ -22,7 +22,7 @@
       },
       "source" : 
       [
-        "Mojo is a high-level programming language with an extensive set of modern features. But Mojo also provides you, the programmer, access to all of the low-level primitives that you need to write powerful -- yet zero-cost -- abstractions.\n",
+        "Mojo is a high-level programming language with an extensive set of modern features. Mojo also provides you, the programmer, access to all of the low-level primitives that you need to write powerful -- yet zero-cost -- abstractions.\n",
         "\n",
         "These primitives are implemented in [MLIR](https://mlir.llvm.org), an extensible intermediate representation (IR) format for compiler design. Many different programming languages and compilers translate their source programs into MLIR, and because Mojo provides direct access to MLIR features, this means Mojo programs can enjoy the benefits of each of these tools.\n",
         "\n",