diff --git a/sdp/challenger2_sdp.py b/sdp/challenger2_sdp.py
new file mode 100644
index 0000000..3072c9e
--- /dev/null
+++ b/sdp/challenger2_sdp.py
@@ -0,0 +1,120 @@
+import numpy as np
+import pyfftw
+
+
+class SLIDING_DOT_PRODUCT:
+    # https://stackoverflow.com/a/30615425/2955541
+    def __init__(self):
+        self.m = 0
+        self.n = 0
+        self.shape = 0
+        self.threads = 1
+
+        self.rfft_obj = None
+        self.irfft_obj = None
+
+        self.store_obj = False
+        self.historical_obj = {}
+
+        self.real_arr = pyfftw.empty_aligned(2**20, dtype=np.float64)
+        self.complex_arr = pyfftw.empty_aligned(2**20, dtype=np.complex128)
+
+    def __call__(self, Q, T):
+        if T.shape[0] == self.n:
+            shape = self.shape
+        else:
+            shape = pyfftw.next_fast_len(T.shape[0])
+
+        self.n = T.shape[0]
+        self.m = Q.shape[0]
+
+        if self.shape != shape:
+            # need to set rfft/irfft objects
+            self.shape = shape
+            rfft_irfft_objects = self.historical_obj.get(self.shape, None)
+
+            if rfft_irfft_objects is not None:
+                self.rfft_obj = rfft_irfft_objects[0]
+                self.irfft_obj = rfft_irfft_objects[1]
+            else:
+                self.rfft_obj = pyfftw.builders.rfft(
+                    pyfftw.empty_aligned(self.shape, dtype=np.float64),
+                    n=self.shape,
+                    overwrite_input=True,
+                    avoid_copy=True,
+                    threads=self.threads,
+                )
+
+                self.irfft_obj = pyfftw.builders.irfft(
+                    pyfftw.empty_aligned(1 + self.shape // 2, dtype=np.complex128),
+                    overwrite_input=True,
+                    avoid_copy=True,
+                    n=self.shape,
+                    threads=self.threads,
+                )
+
+        if self.store_obj:  # pragma: no cover
+            self.historical_obj[self.shape] = (self.rfft_obj, self.irfft_obj)
+
+        rfft_shape = 1 + self.shape // 2
+        if self.shape <= self.real_arr.shape[0]:
+            real_arr = self.real_arr[: self.shape]
+            complex_arr = self.complex_arr[:rfft_shape]
+        else:  # pragma: no cover
+            real_arr = pyfftw.empty_aligned(self.shape, dtype=np.float64)
+            complex_arr = pyfftw.empty_aligned(rfft_shape, dtype=np.complex128)
+
+        real_arr[: self.n] = T
+        real_arr[self.n :] = 0
+        self.rfft_obj(real_arr)
+        complex_arr[:] = self.rfft_obj.output_array
+
+        real_arr[: self.m] = Q[::-1]
+        real_arr[self.m :] = 0
+        self.rfft_obj(real_arr)
+        complex_arr *= self.rfft_obj.output_array
+
+        self.irfft_obj(complex_arr)
+
+        out = self.irfft_obj.output_array[self.m - 1 : self.n]
+
+        return out
+
+    def create_reusable_objects(self, pmin=2, pmax=20):
+        for p in range(pmin, pmax + 1):
+            n = 2**p
+            rfft_obj = pyfftw.builders.rfft(
+                pyfftw.empty_aligned(n, dtype=np.float64),
+                overwrite_input=True,
+                avoid_copy=True,
+                n=n,
+                threads=self.threads,
+            )
+            irfft_obj = pyfftw.builders.irfft(
+                pyfftw.empty_aligned(1 + n // 2, dtype=np.complex128),
+                overwrite_input=True,
+                avoid_copy=True,
+                n=n,
+                threads=self.threads,
+            )
+            self.historical_obj[n] = (rfft_obj, irfft_obj)
+
+        return
+
+
+_sliding_dot_product = SLIDING_DOT_PRODUCT()
+
+
+def setup(Q, T):
+    _sliding_dot_product.create_reusable_objects()
+    _sliding_dot_product(Q, T)
+    return
+
+
+def sliding_dot_product(Q, T):
+    if len(Q) == len(T):
+        out = np.dot(Q, T)
+    else:
+        out = _sliding_dot_product(Q, T)
+
+    return out
diff --git a/sdp/challenger_sdp.py b/sdp/challenger_sdp.py
index ce60e8d..28af4e6 100644
--- a/sdp/challenger_sdp.py
+++ b/sdp/challenger_sdp.py
@@ -1,14 +1,66 @@
 import numpy as np
+import pyfftw
+
+
+class SLIDING_DOT_PRODUCT:
+    # https://stackoverflow.com/a/30615425/2955541
+    def __init__(self):
+        self.m = 0
+        self.n = 0
+        self.threads = 1
+        self.rfft_Q_obj = None
+        self.rfft_T_obj = None
+        self.irfft_obj = None
+
+    def __call__(self, Q, T):
+        if Q.shape[0] != self.m or T.shape[0] != self.n:
+            self.m = Q.shape[0]
+            self.n = T.shape[0]
+            self.shape = pyfftw.next_fast_len(self.n)
+
+            self.rfft_input = pyfftw.empty_aligned(self.shape, dtype=np.float64)
+            self.rfft_obj = pyfftw.builders.rfft(
+                self.rfft_input,
+                overwrite_input=True,
+                avoid_copy=True,
+                n=self.shape,
+                threads=self.threads,
+            )
+
+            self.irfft_input = pyfftw.empty_aligned(
+                1 + self.shape // 2, dtype=np.complex128
+            )
+            self.irfft_obj = pyfftw.builders.irfft(
+                self.irfft_input,
+                overwrite_input=True,
+                avoid_copy=True,
+                n=self.shape,
+                threads=self.threads,
+            )
+
+        self.rfft_input[: self.m] = Q[::-1] / self.shape  # Reverse/flip Q and scale
+        self.rfft_input[self.m :] = 0.0
+        self.rfft_obj.execute()
+        self.irfft_input[:] = self.rfft_obj.output_array
+
+        self.rfft_input[: self.n] = T
+        self.rfft_input[self.n :] = 0.0
+        self.rfft_obj.execute()
+        self.irfft_input *= self.rfft_obj.output_array
+
+        # irfft_input is ready
+        self.irfft_obj.execute()
+
+        return self.irfft_obj.output_array[self.m - 1 : self.n]
+
+
+_sliding_dot_product = SLIDING_DOT_PRODUCT()
 
 
 def setup(Q, T):
+    _sliding_dot_product(Q, T)
     return
 
 
 def sliding_dot_product(Q, T):
-    m = len(Q)
-    l = T.shape[0] - m + 1
-    out = np.empty(l)
-    for i in range(l):
-        out[i] = np.dot(Q, T[i : i + m])
-    return out
+    return _sliding_dot_product(Q, T)
diff --git a/test.py b/test.py
index c72a6b3..81ad854 100644
--- a/test.py
+++ b/test.py
@@ -192,3 +192,37 @@ def test_setup():
                 raise e
 
     return
+
+
+def test_challenger2_power2():
+    # test for case 5. len(T) is power of 2
+    pmin = 3
+    pmax = 13
+
+    from sdp import challenger2_sdp
+
+    challenger2_sdp._sliding_dot_product.create_reusable_objects(
+        pmin=pmin, pmax=pmax - 1
+    )
+    # leaving out pmax to force the algo to create rfft/irfft if not created before
+
+    for q in range(pmin, pmax + 1):
+        n_Q = 2**q
+        for p in range(q, pmax + 1):
+            n_T = 2**p
+            Q = np.random.rand(n_Q)
+            T = np.random.rand(n_T)
+
+            ref = naive_sliding_dot_product(Q, T)
+            comp = challenger2_sdp.sliding_dot_product(Q, T)
+            npt.assert_allclose(comp, ref)
+
+    n_T = 2**16
+    T = np.random.rand(n_T)
+    Q = np.random.rand(n_T - 1)
+
+    ref = naive_sliding_dot_product(Q, T)
+    comp = challenger2_sdp.sliding_dot_product(Q, T)
+    npt.assert_allclose(comp, ref)
+
+    return
diff --git a/timing_OneShot.py b/timing_OneShot.py
new file mode 100755
index 0000000..9f68e8d
--- /dev/null
+++ b/timing_OneShot.py
@@ -0,0 +1,95 @@
+#!/usr/bin/env python
+
+import argparse
+import numpy as np
+import time
+import warnings
+
+import utils
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="./timing.py -noheader -pmin 6 -pmax 23 -pdiff 3 pyfftw challenger"
+    )
+    parser.add_argument("-noheader", default=False, action="store_true")
+    parser.add_argument(
+        "-timeout",
+        default=5.0,
+        type=float,
+        help="Number of seconds to wait for a run before timing out",
+    )
+    parser.add_argument(
+        "-pequal", default=False, action="store_true", help="Compute `len(Q) == len(T)`"
+    )
+    parser.add_argument(
+        "-niter", default=4, type=int, help="Number of iterations to run"
+    )
+    parser.add_argument("-pmin", default=6, type=int, help="Minimum 2^p to use")
+    parser.add_argument("-pmax", default=27, type=int, help="Maximum 2^p to use")
+    parser.add_argument(
+        "-pdiff",
+        default=100,
+        type=int,
+        help="Maximum deviation from the minimum 2^p allowed",
+    )
+    parser.add_argument(
+        "-ignore",
+        default=None,
+        nargs="*",
+        help="Keyword of modules to match and ignore",
+    )
+    parser.add_argument(
+        "include",
+        default=None,
+        nargs="*",
+        help="Keyword of modules to match and include",
+    )
+    args = parser.parse_args()
+
+    modules = utils.import_sdp_mods(args.include, args.ignore)
+
+    noheader = args.noheader
+    timeout = args.timeout
+    if args.pequal:
+        skip_p_equal = 0
+    else:
+        skip_p_equal = 1
+    n_iter = args.niter
+    p_min = args.pmin
+    p_max = args.pmax
+    p_diff = args.pdiff
+
+    if not noheader:
+        print("module,len_Q,len_T,n_iter,time", flush=True)
+
+    start_timing = time.time()
+    for mod in modules:
+        mod_name = mod.__name__.removeprefix("sdp.").removesuffix("_sdp")
+        mod.setup(np.random.rand(2), np.random.rand(2))
+
+        for i in range(p_min, p_max + 1):
+            Q = np.random.rand(2**i)
+
+            j_range = range(i + skip_p_equal, min(i + p_diff + 1, p_max + 1))
+            timing = np.zeros(len(j_range), dtype=np.float64)
+            for _ in range(n_iter):
+                lst = []
+                for j in j_range:
+                    T = np.random.rand(2**j)
+
+                    start = time.time()
+                    mod.sliding_dot_product(Q, T)
+                    diff = time.time() - start
+                    lst.append(diff)
+
+                timing += np.array(lst)
+
+            timing /= n_iter
+
+            for j_index, j in enumerate(j_range):
+                T = np.random.rand(2**j)
+                info = f"{mod_name},{len(Q)},{len(T)},{n_iter}" + f",{timing[j_index]}"
+                print(info, flush=True)
+
+    elapsed_timing = np.round((time.time() - start_timing) / 60.0, 2)
+    warnings.warn(f"Test completed in {elapsed_timing} min")