diff --git a/.github/workflows/ci-mlir-mpi-openmp.yml b/.github/workflows/ci-mlir-mpi-openmp.yml
index 75217e08d3..5277b7f19a 100644
--- a/.github/workflows/ci-mlir-mpi-openmp.yml
+++ b/.github/workflows/ci-mlir-mpi-openmp.yml
@@ -36,7 +36,7 @@ jobs:
       run: |
         pip install -e .[tests]
         pip install mpi4py
-        pip install git+https://github.com/xdslproject/xdsl@210181350d926f91ee5fdb27f0eb5d1cf53a8997
+        pip install git+https://github.com/xdslproject/xdsl@f8bb935880276cf077e0a80f1905105d0a98eb33
 
     - name: Test with MPI + openmp
       run: |
diff --git a/.github/workflows/ci-mlir-mpi.yml b/.github/workflows/ci-mlir-mpi.yml
index b22e68bebd..f5f6be2638 100644
--- a/.github/workflows/ci-mlir-mpi.yml
+++ b/.github/workflows/ci-mlir-mpi.yml
@@ -36,7 +36,7 @@ jobs:
       run: |
         pip install -e .[tests]
         pip install mpi4py
-        pip install git+https://github.com/xdslproject/xdsl@210181350d926f91ee5fdb27f0eb5d1cf53a8997
+        pip install git+https://github.com/xdslproject/xdsl@f8bb935880276cf077e0a80f1905105d0a98eb33
 
     - name: Test with MPI - no Openmp
       run: |
diff --git a/.github/workflows/ci-mlir-openmp.yml b/.github/workflows/ci-mlir-openmp.yml
index bfadf023cb..f8a8e6f04e 100644
--- a/.github/workflows/ci-mlir-openmp.yml
+++ b/.github/workflows/ci-mlir-openmp.yml
@@ -36,8 +36,8 @@ jobs:
       run: |
         pip install -e .[tests]
         pip install mpi4py
-        pip install git+https://github.com/xdslproject/xdsl@210181350d926f91ee5fdb27f0eb5d1cf53a8997
-        
+        pip install git+https://github.com/xdslproject/xdsl@f8bb935880276cf077e0a80f1905105d0a98eb33
+
     - name: Test no-MPI, Openmp
       run: |
           export DEVITO_MPI=0
diff --git a/.github/workflows/ci-mlir.yml b/.github/workflows/ci-mlir.yml
index a1eb856b18..3ed33c97cc 100644
--- a/.github/workflows/ci-mlir.yml
+++ b/.github/workflows/ci-mlir.yml
@@ -36,7 +36,7 @@ jobs:
       run: |
         pip install -e .[tests]
         pip install mpi4py
-        pip install git+https://github.com/xdslproject/xdsl@210181350d926f91ee5fdb27f0eb5d1cf53a8997
+        pip install git+https://github.com/xdslproject/xdsl@f8bb935880276cf077e0a80f1905105d0a98eb33
         
     - name: Test no-MPI, no-Openmp
       run: |
diff --git a/devito/core/cpu.py b/devito/core/cpu.py
index 3f80d5e04e..ecf6dce42f 100644
--- a/devito/core/cpu.py
+++ b/devito/core/cpu.py
@@ -3,13 +3,12 @@
 from devito.core.operator import CoreOperator, CustomOperator, ParTile
 from devito.exceptions import InvalidOperator
 from devito.passes.equations import collect_derivatives
-from devito.tools import timed_pass
-
 from devito.passes.clusters import (Lift, blocking, buffering, cire, cse,
-                                    factorize, fission, fuse, optimize_hyperplanes,
-                                    optimize_pows)
-from devito.passes.iet import (CTarget, OmpTarget, avoid_denormals, hoist_prodders,
-                               linearize, mpiize, relax_incr_dimensions)
+                                    factorize, fission, fuse, optimize_pows,
+                                    optimize_hyperplanes)
+from devito.passes.iet import (CTarget, OmpTarget, avoid_denormals, linearize, mpiize,
+                               hoist_prodders, relax_incr_dimensions)
+from devito.tools import timed_pass
 
 
 __all__ = ['Cpu64NoopCOperator', 'Cpu64NoopOmpOperator', 'Cpu64AdvCOperator',
diff --git a/devito/core/cpu_xdsl.py b/devito/core/cpu_xdsl.py
index 91f52b2ffd..b9ddc5546e 100644
--- a/devito/core/cpu_xdsl.py
+++ b/devito/core/cpu_xdsl.py
@@ -18,7 +18,7 @@
 from devito.logger import info, perf
 from devito.mpi import MPI
 from devito.operator.profiling import create_profile
-from devito.tools import filter_sorted, flatten, OrderedSet
+from devito.tools import filter_sorted, flatten, as_tuple
 from devito.types import TimeFunction
 from devito.types.dense import DiscreteFunction, Function
 from devito.types.mlir_types import f32, ptr_of
@@ -33,6 +33,8 @@
 from devito.passes.iet import CTarget, OmpTarget
 from devito.core.cpu import Cpu64OperatorMixin
 
+from examples.seismic.source import PointSource
+
 __all__ = ['XdslnoopOperator', 'XdslAdvOperator']
 
 
@@ -57,12 +59,12 @@ def _build(cls, expressions, **kwargs):
         Callable.__init__(op, **op.args)
 
         # Header files, etc.
-        op._headers = OrderedSet(*cls._default_headers)
-        op._headers.update(byproduct.headers)
-        op._globals = OrderedSet(*cls._default_globals)
-        op._includes = OrderedSet(*cls._default_includes)
-        op._includes.update(profiler._default_includes)
-        op._includes.update(byproduct.includes)
+        # op._headers = OrderedSet(*cls._default_headers)
+        # op._headers.update(byproduct.headers)
+        # op._globals = OrderedSet(*cls._default_globals)
+        # op._includes = OrderedSet(*cls._default_includes)
+        # op._includes.update(profiler._default_includes)
+        # op._includes.update(byproduct.includes)
 
         # Required for the jit-compilation
         op._compiler = kwargs['compiler']
@@ -94,7 +96,7 @@ def _build(cls, expressions, **kwargs):
         op._dtype, op._dspace = irs.clusters.meta
         op._profiler = profiler
         kwargs['xdsl_num_sections'] = len(FindNodes(Section).visit(irs.iet))
-        module = cls._lower_stencil(irs.expressions, **kwargs)
+        module = cls._lower_stencil(expressions, **kwargs)
         op._module = module
 
         return op
@@ -107,8 +109,8 @@ def _lower_stencil(cls, expressions, **kwargs):
         Apply timers to the module
         """
 
-        conv = ExtractDevitoStencilConversion()
-        module = conv.convert(expressions, **kwargs)
+        conv = ExtractDevitoStencilConversion(cls)
+        module = conv.convert(as_tuple(expressions), **kwargs)
         # print(module)
         apply_timers(module, timed=True, **kwargs)
 
@@ -309,9 +311,9 @@ def cfunction(self):
         if self._cfunction is None:
             self._cfunction = getattr(self._lib, self.name)
             # Associate a C type to each argument for runtime type check
-            argtypes = self._construct_cfunction_args(self._jit_kernel_constants,
-                                                      get_types=True)
-            self._cfunction.argtypes = argtypes
+            # argtypes = self._construct_cfunction_args(self._jit_kernel_constants,
+            # get_types=True)
+            # self._cfunction.argtypes = argtypes
 
         return self._cfunction
 
@@ -356,26 +358,21 @@ def setup_memref_args(self):
                 data = arg._data
                 for t in range(data.shape[0]):
                     args[f'{arg._C_name}{t}'] = data[t, ...].ctypes.data_as(ptr_of(f32))
-            if isinstance(arg, Function):
-                args[f'{arg._C_name}'] = arg._data[...].ctypes.data_as(ptr_of(f32))
+            elif isinstance(arg, Function):
+                args[arg._C_name] = arg._data[...].ctypes.data_as(ptr_of(f32))
+
+            elif isinstance(arg, PointSource):
+                args[arg._C_name] = arg._data[...].ctypes.data_as(ptr_of(f32))
+            else:
+                raise NotImplementedError(f"type {type(arg)} not implemented")
 
         self._jit_kernel_constants.update(args)
 
-    def _construct_cfunction_args(self, args, get_types=False):
-        """
-        Either construct the args for the cfunction, or construct the
-        arg types for it.
-        """
-        ps = {
-            p._C_name: p._C_ctype for p in self.parameters
-        }
+    def _construct_cfunction_types(self, args):
+        ps = {p._C_name: p._C_ctype for p in self.parameters}
 
-        objects = []
         objects_types = []
-
         for name in get_arg_names_from_module(self._module):
-            object = args[name]
-            objects.append(object)
             if name in ps:
                 object_type = ps[name]
                 if object_type == DiscreteFunction._C_ctype:
@@ -383,11 +380,20 @@ def _construct_cfunction_args(self, args, get_types=False):
                 objects_types.append(object_type)
             else:
                 objects_types.append(type(object))
+        return objects_types
+
+    def _construct_cfunction_args(self, args):
+        """
+        Either construct the args for the cfunction, or construct the
+        arg types for it.
+        """
+
+        objects = []
+        for name in get_arg_names_from_module(self._module):
+            object = args[name]
+            objects.append(object)
 
-        if get_types:
-            return objects_types
-        else:
-            return objects
+        return objects
 
 
 class XdslAdvOperator(XdslnoopOperator):
diff --git a/devito/ir/ietxdsl/cluster_to_ssa.py b/devito/ir/ietxdsl/cluster_to_ssa.py
index 4b00463285..c6d9b0ad3d 100644
--- a/devito/ir/ietxdsl/cluster_to_ssa.py
+++ b/devito/ir/ietxdsl/cluster_to_ssa.py
@@ -1,8 +1,22 @@
+from functools import reduce
 # ------------- General imports -------------#
 
 from typing import Any, Iterable
 from dataclasses import dataclass, field
-from sympy import Add, Expr, Float, Indexed, Integer, Mod, Mul, Pow, Symbol
+from sympy import Add, And, Expr, Float, GreaterThan, Indexed, Integer, LessThan, Mod, Mul, Number, Pow, StrictGreaterThan, StrictLessThan, Symbol, floor
+from sympy.core.relational import Relational
+from sympy.logic.boolalg import BooleanFunction
+from devito.ir.equations.equation import OpInc
+from devito.operations.interpolators import Injection
+from devito.operator.operator import Operator
+from devito.symbolics.search import retrieve_dimensions, retrieve_functions
+from devito.symbolics.extended_sympy import INT
+from devito.tools.data_structures import OrderedSet
+from devito.tools.utils import as_tuple
+from devito.types.basic import Scalar
+from devito.types.dense import DiscreteFunction, Function, TimeFunction
+from devito.types.dimension import SpaceDimension, TimeDimension
+from devito.types.equation import Eq
 
 # ------------- xdsl imports -------------#
 from xdsl.dialects import (arith, builtin, func, memref, scf,
@@ -15,8 +29,10 @@
     PatternRewriteWalker,
     RewritePattern,
     op_type_rewrite_pattern,
+    InsertPoint
 )
 from xdsl.builder import ImplicitBuilder
+from xdsl.transforms.experimental.convert_stencil_to_ll_mlir import StencilToMemRefType
 
 # ------------- devito imports -------------#
 from devito import Grid, SteppingDimension
@@ -30,15 +46,31 @@
 # ------------- devito-xdsl SSA imports -------------#
 from devito.ir.ietxdsl import iet_ssa
 from devito.ir.ietxdsl.utils import is_int, is_float
+import numpy as np
+
+from examples.seismic.source import PointSource
+from tests.test_interpolation import points
+from tests.test_timestepping import d
+
+dtypes_to_xdsltypes = {
+    np.float32: builtin.f32,
+    np.float64: builtin.f64,
+    np.int32: builtin.i32,
+    np.int64: builtin.i64,
+}
 
 # flake8: noqa
 
 
 def field_from_function(f: DiscreteFunction) -> stencil.FieldType:
-    halo = [f.halo[d] for d in f.grid.dimensions]
-    shape = f.grid.shape
+    # import pdb;pdb.set_trace()
+    halo = [f.halo[d] for d in f.dimensions]
+    shape = f.shape
     bounds = [(-h[0], s+h[1]) for h, s in zip(halo, shape)]
-    return stencil.FieldType(bounds, element_type=dtype_to_xdsltype(f.dtype))
+    if isinstance(f, TimeFunction):
+        bounds = bounds[1:]
+
+    return stencil.FieldType(bounds, element_type=dtypes_to_xdsltypes[f.dtype])
 
 
 class ExtractDevitoStencilConversion:
@@ -46,6 +78,7 @@ class ExtractDevitoStencilConversion:
     Lower Devito equations to the stencil dialect
     """
 
+    operator: type[Operator]
     eqs: list[LoweredEq]
     block: Block
     temps: dict[tuple[DiscreteFunction, int], SSAValue]
@@ -58,6 +91,10 @@ def __init__(self):
 
     time_offs: int
 
+    def __init__(self, operator: type[Operator]):
+        self.temps = dict()
+        self.operator = operator
+
     def convert_function_eq(self, eq: LoweredEq, **kwargs):
         # Read the grid containing necessary discretization information
         # (size, halo width, ...)
@@ -74,8 +111,20 @@ def convert_function_eq(self, eq: LoweredEq, **kwargs):
         else:
             raise NotImplementedError(f"Function of type {type(write_function)} not supported")
 
-        # Get the function carriers of the equation
-        self._build_step_body(step_dim, eq)
+        dims = retrieve_dimensions(eq.lhs.indices)
+
+        if not all(isinstance(d, (SteppingDimension, SpaceDimension)) for d in dims):
+            self.build_generic_step(step_dim, eq)
+        else:
+            # Get the function carriers of the equation
+            self.build_stencil_step(step_dim, eq)
+
+    def convert_symbol_eq(self, symbol: Symbol, rhs: LoweredEq, **kwargs):
+        """
+        Convert a symbol equation to xDSL.
+        """
+        self.symbol_values[symbol.name] = self._visit_math_nodes(None, rhs, None)
+        self.symbol_values[symbol.name].name_hint = symbol.name
 
     def convert_symbol_eq(self, symbol: Symbol, rhs: LoweredEq, **kwargs):
         """
@@ -116,7 +165,6 @@ def _convert_eq(self, eq: LoweredEq, **kwargs):
         "stencil.store"(%4, %u_t1) {"lb" = #stencil.index<0>, "ub" = #stencil.index<3>} : (!stencil.temp<?xf32>, !stencil.field<[-1,4]xf32>) -> ()
         ```
         """
-
         # Get the left hand side, called "output function" here because it tells us
         # Where to write the results of each step.
         write_function = eq.lhs
@@ -139,18 +187,35 @@ def _visit_math_nodes(self, dim: SteppingDimension, node: Expr,
                           output_indexed: Indexed) -> SSAValue:
         # Handle Indexeds
         if isinstance(node, Indexed):
-            space_offsets = []
-            for d in node.function.space_dimensions:
-                space_offsets.append(node.indices[d] - output_indexed.indices[d])
+            # If we have a time function, we compute its time offset
             if isinstance(node.function, TimeFunction):
                 time_offset = (node.indices[dim] - dim) % node.function.time_size
-            elif isinstance(node.function, Function):
+            elif isinstance(node.function, (Function, PointSource)):
                 time_offset = 0
             else:
-                raise NotImplementedError(f"reading function of type {type(node.func)} not supported")  # noqa
-            temp = self.apply_temps[(node.function, time_offset)]
-            access = stencil.AccessOp.get(temp, space_offsets)
-            return access.res
+                raise NotImplementedError(f"reading function of type {type(node.func)} not supported")
+            # If we are in a stencil (encoded by having the output_indexed passed), we
+            # compute the relative space offsets and make it a stencil offset
+            if output_indexed is not None:
+                space_offsets = [node.indices[d] - output_indexed.indices[d] for d in node.function.space_dimensions]
+                temp = self.function_values[(node.function, time_offset)]
+                access = stencil.AccessOp.get(temp, space_offsets)
+                return access.res
+            # Otherwise, generate a load op
+            else:
+                temp = self.function_values[(node.function, time_offset)]
+                memtemp = builtin.UnrealizedConversionCastOp.get(temp, StencilToMemRefType(temp.type)).results[0]
+                memtemp.name_hint = temp.name_hint + "_mem"
+                indices = node.indices
+                if isinstance(node.function, TimeFunction):
+                    indices = indices[1:]
+                ssa_indices = [self._visit_math_nodes(dim, i, output_indexed) for i in node.indices]
+                for i, ssa_i in enumerate(ssa_indices):
+                    if isinstance(ssa_i.type, builtin.IntegerType):
+                        ssa_indices[i] = arith.IndexCastOp(ssa_i, builtin.IndexType())
+                return memref.Load.get(memtemp, ssa_indices).res
+
+            import pdb; pdb.set_trace()
         # Handle Integers
         elif isinstance(node, Integer):
             cst = arith.Constant.from_int_and_width(int(node), builtin.i64)
@@ -161,8 +226,12 @@ def _visit_math_nodes(self, dim: SteppingDimension, node: Expr,
             return cst.result
         # Handle Symbols
         elif isinstance(node, Symbol):
-            symb = iet_ssa.LoadSymbolic.get(node.name, builtin.f32)
-            return symb.result
+            if node.name in self.symbol_values:
+                return self.symbol_values[node.name]
+            else: 
+                mlir_dtype = dtypes_to_xdsltypes[node.dtype]
+                symb = iet_ssa.LoadSymbolic.get(node.name, mlir_dtype)
+                return symb.result     
         # Handle Add Mul
         elif isinstance(node, (Add, Mul)):
             args = [self._visit_math_nodes(dim, arg, output_indexed) for arg in node.args]
@@ -170,13 +239,13 @@ def _visit_math_nodes(self, dim: SteppingDimension, node: Expr,
             # get first element out, store the rest in args
             # this makes the reduction easier
             carry, *args = self._ensure_same_type(*args)
-            # select the correct op from arith.Addi, arith.Addf, arith.Muli, arith.Mulf
-            if isinstance(carry.type, builtin.IntegerType):
+            # select the correct op from arith.addi, arith.addf, arith.muli, arith.mulf
+            if is_int(carry):
                 op_cls = arith.Addi if isinstance(node, Add) else arith.Muli
             elif isinstance(carry.type, builtin.Float32Type):
                 op_cls = arith.Addf if isinstance(node, Add) else arith.Mulf
             else:
-                raise("Add support for another type")
+                raise NotImplementedError(f"Add support for another type {carry.type}")
             for arg in args:
                 op = op_cls(carry, arg)
                 carry = op.result
@@ -205,12 +274,40 @@ def _visit_math_nodes(self, dim: SteppingDimension, node: Expr,
             op = op_cls(base, ex)
             return op.result
         # Handle Mod
-        elif isinstance(node, Mod):
-            raise NotImplementedError("Go away, no mod here. >:(")
+        elif isinstance(node, INT):
+            assert len(node.args) == 1, "Expected single argument for integer cast."
+            return arith.FPToSIOp(self._visit_math_nodes(dim, node.args[0], output_indexed), builtin.i64).result
+        elif isinstance(node, floor):
+            assert len(node.args) == 1, "Expected single argument for floor."
+            return math.FloorOp(self._visit_math_nodes(dim, node.args[0], output_indexed)).result
+        elif isinstance(node, And):
+            SSAargs = (self._visit_math_nodes(dim, arg, output_indexed) for arg in node.args)
+            return reduce(lambda x,y : arith.AndI(x,y).result, SSAargs)
+        elif isinstance(node, Relational):
+            if isinstance(node, GreaterThan):
+                mnemonic = "sge"
+            elif isinstance(node, LessThan):
+                mnemonic = "sle"
+            elif isinstance(node, StrictGreaterThan):
+                mnemonic = "sgt"
+            elif isinstance(node, StrictLessThan):
+                mnemonic = "slt"
+            else:
+                raise NotImplementedError(f"Unimplemented comparison {type(node)}")
+                
+            # import pdb;
+            # pdb.set_trace()
+            SSAargs = (self._visit_math_nodes(dim, arg, output_indexed) for arg in node.args)
+            # Operands must have the same type
+            # TODO: look at here if index stuff does not make sense
+            # The s in sgt means *signed* greater than
+            # Writer has no clue if this should rather be u for unsigned
+            return arith.Cmpi(*self._ensure_same_type(*SSAargs), mnemonic).result
+
         else:
-            raise NotImplementedError(f"Unknown math: {node}", node)
+            raise NotImplementedError(f"Unknown math:{type(node)} {node}", node)
 
-    def _build_step_body(self, dim: SteppingDimension, eq: LoweredEq) -> None:
+    def build_stencil_step(self, dim: SteppingDimension, eq:LoweredEq) -> None:
         """
         Builds the body of the step function for a given dimension and equation.
 
@@ -223,13 +320,15 @@ def _build_step_body(self, dim: SteppingDimension, eq: LoweredEq) -> None:
         """
         read_functions = set()
         for f in retrieve_function_carriers(eq.rhs):
-            if isinstance(f.function, TimeFunction):
-                time_offset = (f.indices[dim] - dim) % f.function.time_size
+            if isinstance(f.function, PointSource):
+                time_offset = 0
+            elif isinstance(f.function, TimeFunction):
+                time_offset = (f.indices[dim]-dim) % f.function.time_size
             elif isinstance(f.function, Function):
                 time_offset = 0
             else:
-                raise NotImplementedError(f"reading function of type {type(f.func)}"
-                                          "not supported")
+                import pdb;pdb.set_trace()
+                raise NotImplementedError(f"reading function of type {type(f.function)} not supported")
             read_functions.add((f.function, time_offset))
 
         for f, t in read_functions:
@@ -258,7 +357,9 @@ def _build_step_body(self, dim: SteppingDimension, eq: LoweredEq) -> None:
             assert "temp" in apply_op.name_hint
             apply_arg.name_hint = apply_op.name_hint.replace("temp", "blk")
 
-        self.apply_temps = {k: v for k, v in zip(read_functions, apply.region.block.args)}
+        self.apply_temps = {k:v for k,v in zip(read_functions, apply.region.block.args)}
+        # Update the function values with the new temps
+        self.function_values |= self.apply_temps
 
         with ImplicitBuilder(apply.region.block):
             stencil.ReturnOp.get([self._visit_math_nodes(dim, eq.rhs, eq.lhs)])
@@ -277,8 +378,43 @@ def _build_step_body(self, dim: SteppingDimension, eq: LoweredEq) -> None:
         store.temp_with_halo.name_hint = f"{write_function.name}_t{self.out_time_buffer[1]}_temp"  # noqa
         self.temps[self.out_time_buffer] = store.temp_with_halo
 
+    def build_generic_step_expression(self, dim: SteppingDimension, eq: LoweredEq):
+        # Sources
+        value = self._visit_math_nodes(dim, eq.rhs, None)
+        temp = self.function_values[self.out_time_buffer]
+        memtemp = builtin.UnrealizedConversionCastOp.get([temp], [StencilToMemRefType(temp.type)]).results[0]
+        memtemp.name_hint = temp.name_hint + "_mem"
+        indices = eq.lhs.indices
+        if isinstance(eq.lhs.function, TimeFunction):
+            indices = indices[1:]
+        ssa_indices = [self._visit_math_nodes(dim, i, None) for i in indices]
+        for i, ssa_i in enumerate(ssa_indices):
+            if isinstance(ssa_i.type, builtin.IntegerType):
+                ssa_indices[i] = arith.IndexCastOp(ssa_i, builtin.IndexType())
+
+        match eq.operation:
+            case None:
+                memref.Store.get(value, memtemp, ssa_indices)
+            case OpInc:
+                memref.AtomicRMWOp(operands=[value, memtemp, ssa_indices], result_types=[value.type], properties={"kind" : builtin.IntegerAttr(0, builtin.i64)})
+
+    def build_condition(self, dim: SteppingDimension, eq: BooleanFunction):
+        return self._visit_math_nodes(dim, eq, None)
+
+    def build_generic_step(self, dim: SteppingDimension, eq: LoweredEq):
+        if eq.conditionals:
+            condition = And(*eq.conditionals.values(), evaluate=False)
+            cond = self.build_condition(dim, condition)
+            if_ = scf.If(cond, (), Region(Block()))
+            with ImplicitBuilder(if_.true_region.block):
+                self.build_generic_step_expression(dim, eq)
+                scf.Yield()
+        else:
+            # Build the expression
+            self.build_generic_step_expression(dim, eq)
+
     def build_time_loop(
-        self, eqs: list[LoweredEq], step_dim: SteppingDimension, **kwargs
+        self, eqs: list[Any], step_dim: SteppingDimension, **kwargs
     ):
         # Bounds and step boilerpalte
         lb = iet_ssa.LoadSymbolic.get(
@@ -319,7 +455,9 @@ def build_time_loop(
         )
 
         # Name the 'time' step iterator
-        loop.body.block.args[0].name_hint = step_dim.root.name  # 'time'
+        loop.body.block.args[0].name_hint = "time"
+        # Store for later reference
+        self.symbol_values["time"] = loop.body.block.args[0]
 
         # Store a mapping from time_buffers to their corresponding block
         # arguments for easier access later.
@@ -341,10 +479,66 @@ def build_time_loop(
             ]
             scf.Yield(*yield_args)
 
-    def generate_equations(self, eqs: list[LoweredEq], **kwargs):
+    def generate_equations(self, eqs: list[Any], **kwargs):
         # Lower equations to their xDSL equivalent
         for eq in eqs:
-            self._convert_eq(eq, **kwargs)
+            if isinstance(eq, Eq):
+                # Nested lowering? TO re-think approach
+                lowered = self.operator._lower_exprs(as_tuple(eq), **kwargs)
+                for lo in lowered:
+                    self._convert_eq(lo)
+            elif isinstance(eq, Injection):
+                lowered = self.operator._lower_exprs(as_tuple(eq), **kwargs)
+                self._lower_injection(lowered)
+            else:
+                raise NotImplementedError(f"Expression {eq} of type {type(eq)} not supported")
+
+    def _lower_injection(self, eqs: list[LoweredEq]):
+        """
+        Lower an injection to xDSL.
+        """
+        # We assert that all equations of one Injection share the same iteration space!
+        ispaces = [e.ispace for e in eqs]
+        assert all(ispaces[0] == isp for isp in ispaces[1:])
+        ispace = ispaces[0]
+        assert isinstance(ispace.dimensions[0], TimeDimension)
+
+        lbs = []
+        ubs = []
+        for interval in ispace[1:]:
+            lower = interval.symbolic_min
+            if isinstance(lower, Scalar):
+                lb = iet_ssa.LoadSymbolic.get(lower._C_name, builtin.IndexType())
+            elif isinstance(lower, (Number, int)):
+                lb = arith.Constant.from_int_and_width(int(lower), builtin.IndexType())
+            else:
+                raise NotImplementedError(f"Lower bound of type {type(lower)} not supported")
+            lb.result.name_hint = f"{interval.dim.name}_m"
+
+            upper = interval.symbolic_max
+            if isinstance(upper, Scalar):
+                ub = iet_ssa.LoadSymbolic.get(upper._C_name, builtin.IndexType())
+            elif isinstance(upper, (Number, int)):
+                ub = arith.Constant.from_int_and_width(int(upper), builtin.IndexType())
+            else:
+                raise NotImplementedError(
+                    f"Upper bound of type {type(upper)} not supported"
+                )
+            ub.result.name_hint = f"{interval.dim.name}_M"
+            lbs.append(lb)
+            ubs.append(ub)
+
+        steps = [arith.Constant.from_int_and_width(1, builtin.IndexType()).result]*len(ubs)
+        ubs = [arith.Addi(ub, steps[0]) for ub in ubs]
+
+        with ImplicitBuilder(scf.ParallelOp(lbs, ubs, steps, [pblock := Block(arg_types=[builtin.IndexType()]*len(ubs))]).body):
+            for arg, interval in zip(pblock.args, ispace[1:], strict=True):
+                arg.name_hint = interval.dim.name
+                self.symbol_values[interval.dim.name] = arg
+            for eq in eqs:
+                self._convert_eq(eq)
+            scf.Yield()
+            # raise NotImplementedError("Injections not supported yet")
 
     def convert(self, eqs: Iterable[Eq], **kwargs) -> builtin.ModuleOp:
         """
@@ -387,15 +581,33 @@ def convert(self, eqs: Iterable[Eq], **kwargs) -> builtin.ModuleOp:
         calling the operator.
         """
         # Instantiate the module.
-        self.function_values: dict[tuple[Function, int], SSAValue] = {}
+        self.function_values : dict[tuple[Function, int], SSAValue] = {}
+        self.symbol_values : dict[str, SSAValue] = {}
         module = builtin.ModuleOp(Region([block := Block([])]))
         with ImplicitBuilder(block):
             # Get all functions used in the equations
-            functions = OrderedSet(
-                *(f.function for eq in eqs for f in retrieve_function_carriers(eq))
-            )
-            self.time_buffers: list[TimeFunction] = []
-            self.functions: list[Function] = []
+            functions = OrderedSet()
+            for eq in eqs:
+                if isinstance(eq, Eq):
+                    # Use funcs not carriers
+                    funcs = retrieve_functions(eq)
+
+                    for f in funcs:
+                        functions.add(f.function)
+
+                elif isinstance(eq, Injection):
+                    # import pdb; pdb.set_trace()
+                    functions.add(eq.field.function)
+                    for f in retrieve_functions(eq.expr):
+                        if isinstance(f, PointSource):
+                            functions.add(f._coordinates)
+                        functions.add(f.function)
+
+                else:
+                    raise NotImplementedError(f"Expression {eq} of type {type(eq)} not supported")
+
+            self.time_buffers : list[TimeFunction] = []
+            self.functions : list[Function] = []
             for f in functions:
                 match f:
                     case TimeFunction():
@@ -403,6 +615,11 @@ def convert(self, eqs: Iterable[Eq], **kwargs) -> builtin.ModuleOp:
                             self.time_buffers.append((f, i))
                     case Function():
                         self.functions.append(f)
+                    case PointSource():
+                        self.functions.append(f.coordinates)
+                        self.functions.append(f)
+                    case _:
+                        raise NotImplementedError(f"Function of type {type(f)} not supported")
 
             # For each used time_buffer, define a stencil.field type for the function.
             # Those represent DeVito's buffers in xDSL/stencil terms.
@@ -418,33 +635,26 @@ def convert(self, eqs: Iterable[Eq], **kwargs) -> builtin.ModuleOp:
             self.function_args = {}
             for i, (f, t) in enumerate(self.time_buffers):
                 # Also define argument names to help with debugging
-                xdsl_func.body.block.args[i].name_hint = f"{f.name}_vec{t}"
+                xdsl_func.body.block.args[i].name_hint = f._C_name + str(t)
                 self.function_args[(f, t)] = xdsl_func.body.block.args[i]
             for i, f in enumerate(self.functions):
-                xdsl_func.body.block.args[len(self.time_buffers) + i].name_hint = f"{f.name}_vec"  # noqa
-                # tofix what is this 0 in [(f, 0)]
-                self.function_args[(f, 0)] = xdsl_func.body.block.args[len(self.time_buffers) + i]  # noqa
+                # Sources
+                xdsl_func.body.block.args[len(self.time_buffers)+i].name_hint = f._C_name
+                self.function_args[(f, 0)] = xdsl_func.body.block.args[len(self.time_buffers)+i]
 
             # Union operation?
             self.function_values |= self.function_args
-            # print(self.function_values)
 
             # Move on to generate the function body
             with ImplicitBuilder(xdsl_func.body.block):
 
-                # Start building the time loop
-                # TODO: This should be moved to the cluster codegen. In the meantime,
-                # we stick to similar assumptions and just use the first equation's grid
-                # for the time loop information.
-
-                # Get the stepping dimension. It's usually time, and usually the first one.
-                # Getting it here; more readable and less input assumptions :)
-                time_functions = [f for (f, _) in self.time_buffers]
+                # Get the stepping dimension, if there is any in the whole input
+                time_functions = [f for (f,_) in self.time_buffers]
                 dimensions = {
                     d for f in (self.functions + time_functions) for d in f.dimensions
                 }
-                step_dim = next((d for d in dimensions
-                                if isinstance(d, SteppingDimension)), None)
+
+                step_dim = next((d for d in dimensions if isinstance(d, SteppingDimension)), None)
                 if step_dim is not None:
                     self.build_time_loop(eqs, step_dim, **kwargs)
                 else:
@@ -458,12 +668,21 @@ def convert(self, eqs: Iterable[Eq], **kwargs) -> builtin.ModuleOp:
     def _ensure_same_type(self, *vals: SSAValue):
         if all(isinstance(val.type, builtin.IntegerAttr) for val in vals):
             return vals
+        if all(isinstance(val.type, builtin.IndexType) for val in vals):
+            # Sources
+            return vals
         if all(is_float(val) for val in vals):
             return vals
         # not everything homogeneous
+        cast_to_floats = True
+        if all(is_int(val) for val in vals):
+            cast_to_floats = False
         processed = []
         for val in vals:
-            if is_float(val):
+            if cast_to_floats and is_float(val):
+                processed.append(val)
+                continue
+            if (not cast_to_floats) and isinstance(val.type, builtin.IndexType):
                 processed.append(val)
                 continue
             # if the val is the result of a arith.constant with no uses,
@@ -473,14 +692,23 @@ def _ensure_same_type(self, *vals: SSAValue):
                 and isinstance(val.op, arith.Constant)
                 and val.uses == 0
             ):
-                val.type = builtin.f32
-                val.op.attributes["value"] = builtin.FloatAttr(
-                    float(val.op.value.value.data), builtin.f32
-                )
+                if cast_to_floats:
+                    val.type = builtin.f32
+                    val.op.attributes["value"] = builtin.FloatAttr(
+                        float(val.op.value.value.data), builtin.f32
+                    )
+                else:
+                    val.type = builtin.IndexType()
+                    val.op.value.type = builtin.IndexType()
                 processed.append(val)
                 continue
-            # insert an integer to float cast op
-            conv = arith.SIToFPOp(val, builtin.f32)
+            # insert a cast op
+            if cast_to_floats:
+                if val.type == builtin.IndexType():
+                    val = arith.IndexCastOp(val, builtin.i64).result
+                conv = arith.SIToFPOp(val, builtin.f32)
+            else:
+                conv = arith.IndexCastOp(val, builtin.IndexType())
             processed.append(conv.result)
         return processed
 
@@ -531,6 +759,49 @@ def match_and_rewrite(self, op: func.FuncOp, rewriter: PatternRewriter):
         rewriter.insert_op_after_matched_op(wrapper)
 
 
+class TimerRewritePattern(RewritePattern):
+    """
+    Base class for time benchmarking related rewrite patterns
+    """
+    pass
+
+
+@dataclass
+class MakeFunctionTimed(TimerRewritePattern):
+    """
+    Populate the section0 devito timer with the total runtime of the function
+    """
+    func_name: str
+    seen_ops: set[func.Func] = field(default_factory=set)
+
+    @op_type_rewrite_pattern
+    def match_and_rewrite(self, op: func.FuncOp, rewriter: PatternRewriter):
+        if op.sym_name.data != self.func_name or op in self.seen_ops:
+            return
+
+        # only apply once
+        self.seen_ops.add(op)
+
+        # Insert timer start and end calls
+        rewriter.insert_op([
+            t0 := func.Call('timer_start', [], [builtin.f64])
+        ], InsertPoint.at_start(op.body.block))
+
+        ret = op.get_return_op()
+        assert ret is not None
+
+        rewriter.insert_op_before([
+            timers := iet_ssa.LoadSymbolic.get('timers', llvm.LLVMPointerType.opaque()),
+            t1 := func.Call('timer_end', [t0], [builtin.f64]),
+            llvm.StoreOp(t1, timers),
+        ], ret)
+
+        rewriter.insert_op([
+            func.FuncOp.external('timer_start', [], [builtin.f64]),
+            func.FuncOp.external('timer_end', [builtin.f64], [builtin.f64]),
+        ], InsertPoint.after(rewriter.current_operation))
+
+
 def get_containing_func(op: Operation) -> func.FuncOp | None:
     while op is not None and not isinstance(op, func.FuncOp):
         op = op.parent_op()
diff --git a/devito/ir/ietxdsl/profiling.py b/devito/ir/ietxdsl/profiling.py
index 15f0130979..f767a34ff1 100644
--- a/devito/ir/ietxdsl/profiling.py
+++ b/devito/ir/ietxdsl/profiling.py
@@ -6,7 +6,7 @@
 
 from xdsl.pattern_rewriter import (RewritePattern, op_type_rewrite_pattern,
                                    GreedyRewritePatternApplier, PatternRewriter,
-                                   PatternRewriteWalker)
+                                   PatternRewriteWalker, InsertPoint)
 
 
 class TimerRewritePattern(RewritePattern):
@@ -33,9 +33,10 @@ def match_and_rewrite(self, op: func.FuncOp, rewriter: PatternRewriter):
         self.seen_ops.add(op)
 
         # Insert timer start and end calls
-        rewriter.insert_op_at_start([
+        # Insert timer start and end calls
+        rewriter.insert_op([
             t0 := func.Call('timer_start', [], [builtin.f64])
-        ], op.body.block)
+        ], InsertPoint.at_start(op.body.block))
 
         ret = op.get_return_op()
         assert ret is not None
@@ -46,10 +47,10 @@ def match_and_rewrite(self, op: func.FuncOp, rewriter: PatternRewriter):
             llvm.StoreOp(t1, timers),
         ], ret)
 
-        rewriter.insert_op_after_matched_op([
+        rewriter.insert_op([
             func.FuncOp.external('timer_start', [], [builtin.f64]),
-            func.FuncOp.external('timer_end', [builtin.f64], [builtin.f64])
-        ])
+            func.FuncOp.external('timer_end', [builtin.f64], [builtin.f64]),
+        ], InsertPoint.after(rewriter.current_operation))
 
 
 def apply_timers(module, **kwargs):
diff --git a/devito/ir/ietxdsl/utils.py b/devito/ir/ietxdsl/utils.py
index 6d413c8898..04fcbd761b 100644
--- a/devito/ir/ietxdsl/utils.py
+++ b/devito/ir/ietxdsl/utils.py
@@ -3,7 +3,7 @@
 
 
 def is_int(val: SSAValue):
-    return isinstance(val.type, builtin.IntegerType)
+    return isinstance(val.type, (builtin.IntegerType, builtin.IndexType))
 
 
 def is_float(val: SSAValue):
diff --git a/devito/operator/operator.py b/devito/operator/operator.py
index 788e66bd94..1c828a88a0 100644
--- a/devito/operator/operator.py
+++ b/devito/operator/operator.py
@@ -859,8 +859,6 @@ def apply(self, **kwargs):
         # Output summary of performance achieved
         return self._emit_apply_profiling(args)
 
-    # Performance profiling
-
     def _emit_build_profiling(self):
         if not is_log_enabled_for('PERF'):
             return
diff --git a/tests/test_adjoint.py b/tests/test_adjoint.py
index 759b86a3ae..85ab8f2fbc 100644
--- a/tests/test_adjoint.py
+++ b/tests/test_adjoint.py
@@ -79,13 +79,13 @@ class TestAdjoint(object):
         ('layers-viscoacoustic', (20, 25), 'maxwell', 4, 2, viscoacoustic_setup),
         ('layers-viscoacoustic', (20, 25), 'maxwell', 2, 2, viscoacoustic_setup),
         # 3D Deng Mcmechan Viscoacoustic tests with varying space and equation orders
-        ('layers-viscoacoustic', (20, 25, 20), 'maxwell', 4, 1, \
+        ('layers-viscoacoustic', (20, 25, 20), 'maxwell', 4, 1,
             viscoacoustic_setup),
-        ('layers-viscoacoustic', (20, 25, 20), 'maxwell', 2, 1, \
+        ('layers-viscoacoustic', (20, 25, 20), 'maxwell', 2, 1,
             viscoacoustic_setup),
-        ('layers-viscoacoustic', (20, 25, 20), 'maxwell', 4, 2, \
+        ('layers-viscoacoustic', (20, 25, 20), 'maxwell', 4, 2,
             viscoacoustic_setup),
-        ('layers-viscoacoustic', (20, 25, 20), 'maxwell', 2, 2, \
+        ('layers-viscoacoustic', (20, 25, 20), 'maxwell', 2, 2,
             viscoacoustic_setup),
     ])
     def test_adjoint_F(self, mkey, shape, kernel, space_order, time_order, setup_func):
diff --git a/tests/test_xdsl_base.py b/tests/test_xdsl_base.py
index 0e96fba182..0625a8d1e6 100644
--- a/tests/test_xdsl_base.py
+++ b/tests/test_xdsl_base.py
@@ -2,17 +2,18 @@
 import pytest
 
 from devito import (Grid, TensorTimeFunction, VectorTimeFunction, div, grad, diag, solve,
-                    Operator, Eq, Constant, norm, SpaceDimension)
+                    Operator, Eq, Constant, norm, SpaceDimension, switchconfig)
 from devito.types import Array, Function, TimeFunction
 from devito.tools import as_tuple
 
+from examples.seismic.source import RickerSource, TimeAxis
 from xdsl.dialects.scf import For, Yield
 from xdsl.dialects.arith import Addi
+from xdsl.dialects.arith import Constant as xdslconstant
 from xdsl.dialects.func import Call, Return
 from xdsl.dialects.stencil import FieldType, ApplyOp, LoadOp, StoreOp
 from xdsl.dialects.llvm import LLVMPointerType
-
-from examples.seismic.source import RickerSource, TimeAxis
+from xdsl.dialects.memref import Load
 
 
 def test_xdsl_I():
@@ -251,6 +252,160 @@ def test_standard_mlir_rewrites(shape, so, to, nt):
     xdslop.apply(time=nt, dt=dt)
 
 
+class TestSources:
+
+    @switchconfig(openmp=False)
+    @pytest.mark.parametrize('shape', [(8, 8), (38, 38), ])
+    @pytest.mark.parametrize('tn', [20, 80])
+    @pytest.mark.parametrize('factor', [-0.1, 0.0, 0.1, 0.5, 1.1])
+    @pytest.mark.parametrize('factor2', [-0.1, 0.1, 0.5, 1.1])
+    def test_source_only(self, shape, tn, factor, factor2):
+        spacing = (10.0, 10.0)
+        extent = tuple(np.array(spacing) * (shape[0] - 1))
+        origin = (0.0, 0.0)
+
+        v = np.empty(shape, dtype=np.float32)
+        v[:, :51] = 1.5
+        v[:, 51:] = 2.5
+
+        grid = Grid(shape=shape, extent=extent, origin=origin)
+
+        t0 = 0.0
+        # Comes from args
+        tn = tn
+        dt = 1.6
+        time_range = TimeAxis(start=t0, stop=tn, step=dt)
+
+        f0 = 0.010
+        src = RickerSource(name="src", grid=grid, f0=f0, npoint=5, time_range=time_range)
+
+        domain_size = np.array(extent)
+
+        src.coordinates.data[0, :] = domain_size * factor
+        src.coordinates.data[0, -1] = 19.0 * factor2
+
+        u = TimeFunction(name="u", grid=grid, space_order=2)
+        m = Function(name='m', grid=grid)
+        m.data[:] = 1./(v*v)
+
+        src_term = src.inject(field=u.forward, expr=src * dt**2 / m)
+
+        op = Operator([src_term], opt="advanced")
+        op(time=time_range.num-1, dt=dt)
+        normdv = np.linalg.norm(u.data[0])
+        u.data[:, :] = 0
+
+        opx = Operator([src_term], opt="xdsl")
+        opx(time=time_range.num-1, dt=dt)
+        normxdsl = np.linalg.norm(u.data[0])
+
+        assert np.isclose(normdv, normxdsl, rtol=1e-04)
+
+    @switchconfig(openmp=False)
+    @pytest.mark.parametrize('shape', [(8, 8)])
+    @pytest.mark.parametrize('tn', [20, 80])
+    @pytest.mark.parametrize('factor', [-0.1, 0.0, 0.1, 0.5, 1.1])
+    @pytest.mark.parametrize('factor2', [-0.1, 0.1, 0.5, 1.1])
+    def test_source_structure(self, shape, tn, factor, factor2):
+        spacing = (10.0, 10.0)
+        extent = tuple(np.array(spacing) * (shape[0] - 1))
+        origin = (0.0, 0.0)
+
+        v = np.empty(shape, dtype=np.float32)
+        v[:, :51] = 1.5
+        v[:, 51:] = 2.5
+
+        grid = Grid(shape=shape, extent=extent, origin=origin)
+
+        t0 = 0.0
+        # Comes from args
+        tn = tn
+        dt = 1.6
+        time_range = TimeAxis(start=t0, stop=tn, step=dt)
+
+        f0 = 0.010
+        src = RickerSource(name="src", grid=grid, f0=f0, npoint=5, time_range=time_range)
+
+        domain_size = np.array(extent)
+
+        src.coordinates.data[0, :] = domain_size * factor
+        src.coordinates.data[0, -1] = 19.0 * factor2
+
+        u = TimeFunction(name="u", grid=grid, space_order=2)
+        m = Function(name='m', grid=grid)
+        m.data[:] = 1./(v*v)
+
+        src_term = src.inject(field=u.forward, expr=src * dt**2 / m)
+
+        opx = Operator([src_term], opt="xdsl")
+        opx(time=time_range.num-1, dt=dt)
+
+        # Code structure
+        calls = sum(isinstance(op, Call) for op in opx._module.walk())
+        assert calls == 2
+        fors = sum(isinstance(op, For) for op in opx._module.walk())
+        assert fors == 1
+        loads = [op for op in opx._module.walk() if isinstance(op, Load)]
+        assert len(loads) == 8
+        consts = [op for op in opx._module.walk() if isinstance(op, xdslconstant)]
+        assert len(consts) == 65
+
+    @switchconfig(openmp=False)
+    @pytest.mark.parametrize('shape', [(38, 38), ])
+    @pytest.mark.parametrize('tn', [20, 80])
+    @pytest.mark.parametrize('factor', [0.5, 0.8])
+    @pytest.mark.parametrize('factor2', [0.5, 0.8])
+    def test_forward_src_stencil(self, shape, tn, factor, factor2):
+        spacing = (10.0, 10.0)
+        extent = tuple(np.array(spacing) * (shape[0] - 1))
+        origin = (0.0, 0.0)
+
+        v = np.empty(shape, dtype=np.float32)
+        v[:, :51] = 1.5
+        v[:, 51:] = 2.5
+
+        grid = Grid(shape=shape, extent=extent, origin=origin)
+
+        t0 = 0.0
+        # Comes from args
+        tn = tn
+        dt = 1.6
+        time_range = TimeAxis(start=t0, stop=tn, step=dt)
+
+        f0 = 0.010
+        src = RickerSource(name="src", grid=grid, f0=f0, npoint=5, time_range=time_range)
+
+        domain_size = np.array(extent)
+
+        src.coordinates.data[0, :] = domain_size * factor
+        src.coordinates.data[0, -1] = 100.0 * factor2
+
+        u = TimeFunction(name="u", grid=grid, space_order=2, time_order=2)
+        m = Function(name='m', grid=grid)
+        m.data[:] = 1./(v*v)
+
+        src_term = src.inject(field=u.forward, expr=src * dt**2 / m)
+
+        pde = u.dt2 - u.laplace
+        eq0 = solve(pde, u.forward)
+        stencil = Eq(u.forward, eq0)
+
+        op = Operator([stencil, src_term], opt="advanced")
+        op(time=time_range.num-1, dt=dt)
+        # normdv = norm(u)
+        normdv = np.linalg.norm(u.data)
+
+        u.data[:, :] = 0
+
+        opx = Operator([stencil, src_term], opt="xdsl")
+        opx(time=time_range.num-1, dt=dt)
+        normxdsl = np.linalg.norm(u.data)
+        # normxdsl = norm(u)
+
+        assert not np.isclose(normdv, 0.0, rtol=1e-04)
+        assert np.isclose(normdv, normxdsl, rtol=1e-04)
+
+
 def test_xdsl_mul_eqs_I():
     # Define a Devito Operator with multiple eqs
     grid = Grid(shape=(4, 4))
@@ -907,8 +1062,11 @@ def test_elastic_2D(self, shape, so, nt):
         op = Operator([u_v] + [u_t] + src_xx + src_zz)
         op(dt=dt)
 
-        op = Operator([u_v] + [u_t], opt='xdsl')
-        op(dt=dt, time_M=nt)
+        opx = Operator([u_v] + [u_t], opt='xdsl')
+        opx(dt=dt, time_M=nt)
+
+        store_ops = [op for op in opx._module.walk() if isinstance(op, StoreOp)]
+        assert len(store_ops) == 5
 
         xdsl_norm_v0 = norm(v[0])
         xdsl_norm_v1 = norm(v[1])