fix: improved query profile creation to fix avx512 bug without using keep

CHANGELOG.md

@@ -1,6 +1,10 @@
 
 # Next
 
+# v1.2.2 (06-25-2025)
+
+- Improved fix for [AVX512](https://github.com/BioRadOpenSource/ish/issues/50) without using `keep`
+
 # v1.2.1 (06-25-2025)
 
 - Fixed bug in logging module related to update from Mojo 24.3 to 24.4 replacing `write_args`.

ishlib/matcher/alignment/local_aln/striped.mojo

@@ -118,24 +118,29 @@ struct Profile[SIMD_U8_WIDTH: Int, SIMD_U16_WIDTH: Int]:
         var length = Int(score_matrix.size * segment_length)
         var profile = AlignedMemory[T, size, size](length)
 
-        # Generate query profile and rearrange query sequence and calculate the weight of match/mismatch
         var p = profile.as_span()
-        var t_idx = 0
+
+        var bias_typed = bias.cast[T]()
+
         for nt in range(0, score_matrix.size):
+            var nt_base_idx = nt * segment_length
+
             for i in range(0, segment_length):
-                var j = i
-                for segment_idx in range(0, size):
-                    keep(t_idx)
-                    keep(segment_idx)
-                    p[t_idx][segment_idx] = (
-                        bias if j
-                        >= len(query) else (
-                            score_matrix.get(nt, Int(query[j]))
-                            + bias.cast[DType.int8]()
-                        ).cast[DType.uint8]()
-                    ).cast[T]()
-                    j += segment_length
-                t_idx += 1
+                var simd_vector = SIMD[T, size]()
+
+                @parameter
+                for lane in range(size):
+                    var query_pos = i + lane * segment_length
+                    if query_pos < len(query):
+                        var score = score_matrix.get(nt, Int(query[query_pos]))
+                        simd_vector[lane] = (
+                            score + bias.cast[DType.int8]()
+                        ).cast[T]()
+                    else:
+                        simd_vector[lane] = bias_typed
+
+                p[nt_base_idx + i] = simd_vector
+
         return profile
 
 
@@ -294,7 +299,9 @@ fn sw[
     p_vecs.init_columns(len(reference))
     var max_score = UInt8(0).cast[dt]()
     var end_query: Int32 = query_len - 1
-    var end_reference: Int32 = -1  # 0 based best alignment ending point; initialized as isn't aligned -1
+    var end_reference: Int32 = (
+        -1
+    )  # 0 based best alignment ending point; initialized as isn't aligned -1
     var segment_length = p_vecs.segment_length
 
     # Note:

ishlib/matcher/alignment/semi_global_aln/striped.mojo

@@ -107,24 +107,29 @@ struct Profile[
         var length = Int(score_matrix.size * segment_length)
         var profile = AlignedMemory[T, size, size](length)
 
-        # Generate query profile and rearrange query sequence and calculate the weight of match/mismatch
         var p = profile.as_span()
-        var t_idx = 0
+
+        var bias_typed = bias.cast[T]()
+
         for nt in range(0, score_matrix.size):
+            var nt_base_idx = nt * segment_length
+
             for i in range(0, segment_length):
-                var j = i
-                for segment_idx in range(0, size):
-                    keep(t_idx)
-                    keep(segment_idx)
-                    p[t_idx][segment_idx] = (
-                        bias if j
-                        >= len(query) else (
-                            score_matrix.get(nt, Int(query[j]))
-                            + bias.cast[DType.int8]()
-                        ).cast[DType.uint8]()
-                    ).cast[T]()
-                    j += segment_length
-                t_idx += 1
+                var simd_vector = SIMD[T, size]()
+
+                @parameter
+                for lane in range(size):
+                    var query_pos = i + lane * segment_length
+                    if query_pos < len(query):
+                        var score = score_matrix.get(nt, Int(query[query_pos]))
+                        simd_vector[lane] = (
+                            score + bias.cast[DType.int8]()
+                        ).cast[T]()
+                    else:
+                        simd_vector[lane] = bias_typed
+
+                p[nt_base_idx + i] = simd_vector
+
         return profile
 
 
@@ -287,7 +292,9 @@ fn semi_global_aln[
         return AlignmentResult(AlignmentEnd(0, 0, 0))
 
     var end_query: Int32 = query_len - 1
-    var end_reference: Int32 = -1  # 0 based best alignment ending point; initialized as isn't aligned -1
+    var end_reference: Int32 = (
+        -1
+    )  # 0 based best alignment ending point; initialized as isn't aligned -1
     var segment_length = (query_len + width - 1) // width
     var offset = (query_len - 1) % segment_length
     var position = (width - 1) - (query_len - 1) // segment_length
@@ -413,13 +420,13 @@ fn semi_global_aln[
         # Possible speedup - check if v_f has any updates to start with
         var break_out = False
         for _k in range(0, width):
-            var tmp = (ZERO - gap_open_penalty).cast[
-                DType.int32
-            ]() if free_target_start_gaps else (
-                boundary[i + 1] - gap_open_penalty
-            ).cast[
-                DType.int32
-            ]()
+            var tmp = (
+                (ZERO - gap_open_penalty)
+                .cast[DType.int32]() if free_target_start_gaps else (
+                    boundary[i + 1] - gap_open_penalty
+                )
+                .cast[DType.int32]()
+            )
             var tmp2 = MIN if tmp < Int32(MIN) else tmp.cast[dt]()
 
             v_f = v_f.shift_right[1]()

pixi.toml

@@ -12,7 +12,7 @@ preview = ["pixi-build"]
 
 [package]
 name = "ish"
-version = "1.2.1"
+version = "1.2.2"
 license = "Apache-2.0"
 
 [package.build]

recipe.yaml

@@ -1,7 +1,7 @@
 # yaml-language-server: $schema=https://raw.githubusercontent.com/prefix-dev/recipe-format/main/schema.json
 
 context:
-  version: "1.2.1"
+  version: "1.2.2"
   modular_version: "=25.4"
   extramojo_version: "=0.15"