diff --git a/cunumeric/deferred.py b/cunumeric/deferred.py
index 8e6d8cacbd..b04b3088c0 100644
--- a/cunumeric/deferred.py
+++ b/cunumeric/deferred.py
@@ -35,7 +35,7 @@
 
 import legate.core.types as ty
 import numpy as np
-from legate.core import Future, ReductionOp, Store
+from legate.core import Annotation, Future, ReductionOp, Store
 from numpy.core.numeric import normalize_axis_tuple  # type: ignore
 from typing_extensions import ParamSpec
 
@@ -2961,20 +2961,21 @@ def unary_op(
         lhs = self.base
         rhs = src._broadcast(lhs.shape)
 
-        task = self.context.create_auto_task(CuNumericOpCode.UNARY_OP)
-        task.add_output(lhs)
-        task.add_input(rhs)
-        task.add_scalar_arg(op.value, ty.int32)
-        self.add_arguments(task, args)
+        with Annotation(self.context, {"OpCode": op.name}):
+            task = self.context.create_auto_task(CuNumericOpCode.UNARY_OP)
+            task.add_output(lhs)
+            task.add_input(rhs)
+            task.add_scalar_arg(op.value, ty.int32)
+            self.add_arguments(task, args)
 
-        task.add_alignment(lhs, rhs)
+            task.add_alignment(lhs, rhs)
 
-        if multiout is not None:
-            for out in multiout:
-                task.add_output(out.base)
-                task.add_alignment(out.base, rhs)
+            if multiout is not None:
+                for out in multiout:
+                    task.add_output(out.base)
+                    task.add_alignment(out.base, rhs)
 
-        task.execute()
+            task.execute()
 
     # Perform a unary reduction operation from one set of dimensions down to
     # fewer
@@ -3010,10 +3011,6 @@ def unary_reduction(
                 0 if keepdims else lhs_array.ndim
             )
 
-            task = self.context.create_auto_task(
-                CuNumericOpCode.SCALAR_UNARY_RED
-            )
-
             if initial is not None:
                 assert not argred
                 fill_value = initial
@@ -3026,14 +3023,21 @@ def unary_reduction(
             while lhs.ndim > 1:
                 lhs = lhs.project(0, 0)
 
-            task.add_reduction(lhs, _UNARY_RED_TO_REDUCTION_OPS[op])
-            task.add_input(rhs_array.base)
-            task.add_scalar_arg(op, ty.int32)
-            task.add_scalar_arg(rhs_array.shape, (ty.int64,))
+            with Annotation(
+                self.context, {"OpCode": op.name, "ArgRed?": str(argred)}
+            ):
+                task = self.context.create_auto_task(
+                    CuNumericOpCode.SCALAR_UNARY_RED
+                )
 
-            self.add_arguments(task, args)
+                task.add_reduction(lhs, _UNARY_RED_TO_REDUCTION_OPS[op])
+                task.add_input(rhs_array.base)
+                task.add_scalar_arg(op, ty.int32)
+                task.add_scalar_arg(rhs_array.shape, (ty.int64,))
 
-            task.execute()
+                self.add_arguments(task, args)
+
+                task.execute()
 
         else:
             # Before we perform region reduction, make sure to have the lhs
@@ -3062,18 +3066,21 @@ def unary_reduction(
                     "Need support for reducing multiple dimensions"
                 )
 
-            task = self.context.create_auto_task(CuNumericOpCode.UNARY_RED)
+            with Annotation(
+                self.context, {"OpCode": op.name, "ArgRed?": str(argred)}
+            ):
+                task = self.context.create_auto_task(CuNumericOpCode.UNARY_RED)
 
-            task.add_input(rhs_array.base)
-            task.add_reduction(result, _UNARY_RED_TO_REDUCTION_OPS[op])
-            task.add_scalar_arg(axis, ty.int32)
-            task.add_scalar_arg(op, ty.int32)
+                task.add_input(rhs_array.base)
+                task.add_reduction(result, _UNARY_RED_TO_REDUCTION_OPS[op])
+                task.add_scalar_arg(axis, ty.int32)
+                task.add_scalar_arg(op, ty.int32)
 
-            self.add_arguments(task, args)
+                self.add_arguments(task, args)
 
-            task.add_alignment(result, rhs_array.base)
+                task.add_alignment(result, rhs_array.base)
 
-            task.execute()
+                task.execute()
 
         if argred:
             self.unary_op(
@@ -3107,18 +3114,19 @@ def binary_op(
         rhs1 = src1._broadcast(lhs.shape)
         rhs2 = src2._broadcast(lhs.shape)
 
-        # Populate the Legate launcher
-        task = self.context.create_auto_task(CuNumericOpCode.BINARY_OP)
-        task.add_output(lhs)
-        task.add_input(rhs1)
-        task.add_input(rhs2)
-        task.add_scalar_arg(op_code.value, ty.int32)
-        self.add_arguments(task, args)
+        with Annotation(self.context, {"OpCode": op_code.name}):
+            # Populate the Legate launcher
+            task = self.context.create_auto_task(CuNumericOpCode.BINARY_OP)
+            task.add_output(lhs)
+            task.add_input(rhs1)
+            task.add_input(rhs2)
+            task.add_scalar_arg(op_code.value, ty.int32)
+            self.add_arguments(task, args)
 
-        task.add_alignment(lhs, rhs1)
-        task.add_alignment(lhs, rhs2)
+            task.add_alignment(lhs, rhs1)
+            task.add_alignment(lhs, rhs2)
 
-        task.execute()
+            task.execute()
 
     @auto_convert("src1", "src2")
     def binary_reduction(