Loop pass

.gitignore

@@ -1 +1,4 @@
 build
+_dump*
+.vscode
+temp*

CMakeLists.txt

@@ -1,18 +1,20 @@
 cmake_minimum_required(VERSION 3.14)
 project(Vectron)
 
-set(CMAKE_CXX_STANDARD 17)
-set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pedantic -Wall -Wno-return-type-c-linkage")
+set(CMAKE_CXX_STANDARD 20)
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pedantic -fvisibility-inlines-hidden -Wno-return-type-c-linkage -Wno-gnu-zero-variadic-macro-arguments -Wno-deprecated-declarations")
 set(CMAKE_CXX_FLAGS_DEBUG "-g -fno-limit-debug-info")
 set(CMAKE_CXX_FLAGS_RELEASE "-O3")
 include_directories(.)
 
 set(CPM_DOWNLOAD_VERSION 0.32.3)
 set(CPM_DOWNLOAD_LOCATION "${CMAKE_BINARY_DIR}/cmake/CPM_${CPM_DOWNLOAD_VERSION}.cmake")
-if(NOT (EXISTS ${CPM_DOWNLOAD_LOCATION}))
+
+if(NOT(EXISTS ${CPM_DOWNLOAD_LOCATION}))
     message(STATUS "Downloading CPM.cmake...")
     file(DOWNLOAD https://github.com/TheLartians/CPM.cmake/releases/download/v${CPM_DOWNLOAD_VERSION}/CPM.cmake ${CPM_DOWNLOAD_LOCATION})
 endif()
+
 include(${CPM_DOWNLOAD_LOCATION})
 
 CPMAddPackage(
@@ -22,7 +24,8 @@ CPMAddPackage(
     GIT_TAG e7da44d5151e21f153925781ad29334ae0786101
     EXCLUDE_FROM_ALL YES
     OPTIONS "BUILD_SHARED_LIBS OFF"
-            "CMAKE_POSITION_INDEPENDENT_CODE ON")
+    "CMAKE_POSITION_INDEPENDENT_CODE ON")
+
 if(xz_ADDED)
     set_target_properties(xz PROPERTIES EXCLUDE_FROM_ALL ON)
     set_target_properties(xzdec PROPERTIES EXCLUDE_FROM_ALL ON)
@@ -40,27 +43,24 @@ set(VECTRON_FILES
     ir/env_select.cpp
     ir/env_select.h
     ir/vectron.cpp
+    ir/loop.cpp
+    ir/loop.h
     ir/vectron.h)
 add_library(vectron SHARED ${VECTRON_FILES})
 
 # Include directories for your custom LLVM
-target_include_directories(vectron PRIVATE
-    "${CODON_PATH}/include"
-    "${LLVM_PATH}/include")
+target_include_directories(vectron PRIVATE "${CODON_PATH}/include")
 
 # Link directories and libraries for your custom LLVM
-target_link_directories(vectron PRIVATE
-    "${CODON_PATH}/lib/codon"
-    "${LLVM_PATH}/lib")
+target_link_directories(vectron PRIVATE "${CODON_PATH}/lib/codon")
 
-target_link_libraries(vectron PRIVATE codonrt codonc LLVMCore LLVMSupport LLVMTarget)
+target_link_libraries(vectron PRIVATE codonrt codonc) # LLVMCore LLVMSupport LLVMTarget
 
 if(CMAKE_INSTALL_PREFIX_INITIALIZED_TO_DEFAULT)
-  set(CMAKE_INSTALL_PREFIX "${CODON_PATH}/lib/codon" CACHE PATH "Use the existing Codon installation" FORCE)
+    set(CMAKE_INSTALL_PREFIX "${CODON_PATH}/lib/codon" CACHE PATH "Use the existing Codon installation" FORCE)
 endif(CMAKE_INSTALL_PREFIX_INITIALIZED_TO_DEFAULT)
 
 install(TARGETS vectron DESTINATION plugins/vectron/build)
 install(FILES plugin.toml DESTINATION plugins/vectron)
 install(FILES stdlib/experimental/simd.codon DESTINATION stdlib/experimental)
 install(DIRECTORY ${CMAKE_SOURCE_DIR}/stdlib DESTINATION plugins/vectron)
-

applications/v1.0/hamming_distance.codon

@@ -0,0 +1,40 @@
+import time
+import sys
+from vectron.dispatcher import *
+
+var_type = "i16"
+
+@vectron_kernel
+def hamming(t, q):
+    M = [[0 for i in range(len(q) + 1)] for j in range(len(t) + 1)]
+    for i in range(1, len(q) + 1):
+        for j in range(1, len(t) + 1):
+            if j - i <= -1 or j - i >= 1:
+                if j - i == -1 or j - i == 1:
+                    M[i][j] = -10000
+            else:
+                M[i][j] = max(
+                    M[i - 1][j - 1] + (0 if q[i - 1] == t[j - 1] else 1),
+                    0, 0
+                )
+    return M[-1][-1]
+
+@vectron_scheduler
+def invoke(x, y):
+    score = [[0 for _ in range(len(y))] for __ in range(len(x))]
+    for i in range(len(x)):
+        for j in range(len(y)):
+            score[i][j] = hamming(x[i], y[j])
+    return score
+
+with open(sys.argv[-1], 'r') as file:
+    seqs_x = [line.strip() for line in file]
+
+with open(sys.argv[-2], 'r') as file:
+    seqs_y = [line.strip() for line in file]
+
+SEQ_NO_T = len(seqs_x)
+SEQ_NO_Q = len(seqs_y)
+
+with time.timing("Total: "):
+    d = invoke(seqs_x, seqs_y)

applications/v1.0/lcs.codon

@@ -0,0 +1,35 @@
+import time
+import sys
+from vectron.dispatcher import *
+
+var_type = "i16"
+
+@vectron_kernel
+def lcs(t, q):
+    M = [[0 for __ in range(len(q) + 1)] for _ in range(len(t) + 1)]
+    for i in range(1, len(q) + 1):
+        for j in range(1, len(t) + 1):       
+            M[i][j] = max(M[i - 1][j - 1] + (1 if q[i - 1] == t[j - 1] else 0), 
+                        M[i - 1][j], 
+                        M[i][j - 1])
+    return M[-1][-1]
+
+@vectron_scheduler
+def invoke(x, y):
+    score = [[0 for _ in range(len(y))] for __ in range(len(x))]
+    for i in range(len(x)):
+        for j in range(len(y)):
+            score[i][j] = lcs(x[i], y[j])
+    return score
+
+with open(sys.argv[-1], 'r') as file:
+    seqs_x = [line.strip() for line in file]
+
+with open(sys.argv[-2], 'r') as file:
+    seqs_y = [line.strip() for line in file]
+
+SEQ_NO_T = len(seqs_x)
+SEQ_NO_Q = len(seqs_y)
+
+with time.timing("Total: "):
+    d = invoke(seqs_x, seqs_y)

applications/v1.0/levenshtein_distance.codon

@@ -0,0 +1,47 @@
+import time
+import sys
+from vectron.dispatcher import *
+
+var_type = "i16"
+
+# Use vectron to annotate DP kernel
+@vectron_kernel
+def levenshtein(Q, T):
+  M = [[0] * (len(Q) + 1) for _ in range(len(T) + 1)]
+  for i in range(len(T) + 1):
+    M[i][0] = -2 + i * -4
+  for j in range(len(Q) + 1):
+    M[0][j] = -2 + j * -4
+
+  # Kernel
+  for i in range(1, len(T)+1):
+    for j in range(1, len(Q)+1):
+      M[i][j] = max(
+        M[i - 1][j] - 2,
+        M[i][j - 1] - 2,
+        M[i-1][j-1] + (2 if Q[j-1] == T[i-1] else -3)
+      )
+
+  # Aggregation
+  return M[-1][-1]
+
+# Use vectron_scheduler to annotate function that invokes the kernel on list of pairs
+@vectron_scheduler
+def invoke(x, y):
+    score = [[0 for i in range(len(y))] for j in range(len(x))]
+    for i in range(len(x)):
+        for j in range(len(y)):
+            score[i][j] = levenshtein(x[i], y[j])
+    return score
+
+with open(sys.argv[-1], 'r') as file:
+    seqs_x = [line.strip() for line in file]
+
+with open(sys.argv[-2], 'r') as file:
+    seqs_y = [line.strip() for line in file]
+
+SEQ_NO_T = len(seqs_x)
+SEQ_NO_Q = len(seqs_y)
+
+with time.timing("Total: "):
+    d = invoke(seqs_x, seqs_y)

applications/v1.0/manhattan_tourist.codon

@@ -0,0 +1,51 @@
+import time
+import sys
+from vectron.dispatcher import *
+
+var_type = "i16"
+
+
+@vectron_cmp
+def S(x, a, b, am, y):
+    """
+    Due to ambiguous elements ('N'), a specialized match function has been used
+    instead of a ternary operator, hence the "@vectron.cmp" decorator.
+    """
+    if str(x) == "N" or str(y) == "N":
+        return am
+    elif x == y:
+        return a
+    else:
+        return b
+
+@vectron_kernel
+def manhattan(t, q):
+    M = [[0 for i in range(len(q) + 1)] for j in range(len(t) + 1)]
+    for i in range(1, len(q) + 1):
+        for j in range(1, len(t) + 1):
+            M[i][j] = max(
+                M[i][j - 1] + S(q[i - 1], 4, 1, 0, t[j - 1]),
+                0,
+                M[i - 1][j] + S(q[i - 1], 5, 3, 1, t[j - 1])
+            )
+    return M[-1][-1]
+
+@vectron_scheduler
+def invoke(x, y):
+    score = [[0 for _ in range(len(y))] for __ in range(len(x))]
+    for i in range(len(x)):
+        for j in range(len(y)):
+            score[i][j] = manhattan(x[i], y[j])
+    return score
+
+with open(sys.argv[-1], 'r') as file:
+    seqs_x = [line.strip() for line in file]
+
+with open(sys.argv[-2], 'r') as file:
+    seqs_y = [line.strip() for line in file]
+
+SEQ_NO_T = len(seqs_x)
+SEQ_NO_Q = len(seqs_y)
+
+with time.timing("Total: "):
+    d = invoke(seqs_x, seqs_y)

applications/v1.0/min_cost_path.codon

@@ -0,0 +1,52 @@
+import time
+import sys
+from vectron.dispatcher import *
+
+var_type = "i16"
+
+
+@vectron_cmp
+def S(x, a, b, am, y):
+    """
+    Due to ambiguous elements ('N'), a specialized match function has been used
+    instead of a ternary operator, hence the "@vectron.cmp" decorator.
+    """
+    if str(x) == "N" or str(y) == "N":
+        return am
+    elif x == y:
+        return a
+    else:
+        return b
+
+@vectron_kernel
+def min_cost_path(h, m):
+    q = [[0 if (i == 0 and j == 0) else (1 + i * (2)) if (j == 0 and i > 0) else (1 + j * (3)) if (i == 0 and j > 0) else 0 for j in range(len(m) + 1)] for i in range(len(h) + 1)]
+    for i in range(1, len(m) + 1):
+        for j in range(1, len(h) + 1):
+            q[i][j] = max(
+                q[i - 1][j - 1] + S(m[i - 1], 2, -3, -1, h[j - 1]), 
+                q[i - 1][j] + S(m[i - 1], 1, -2, -1, h[j - 1]), 
+                q[i][j - 1] + S(m[i - 1], 1, -2, -2, h[j - 1])
+                )
+
+    return q[-1][-1]
+
+@vectron_scheduler
+def invoke(x, y):
+    score = [[0 for _ in range(len(y))] for __ in range(len(x))]
+    for i in range(len(x)):
+        for j in range(len(y)):
+            score[i][j] = min_cost_path(x[i], y[j])
+    return score
+
+with open(sys.argv[-1], 'r') as file:
+    seqs_x = [line.strip() for line in file]
+
+with open(sys.argv[-2], 'r') as file:
+    seqs_y = [line.strip() for line in file]
+
+SEQ_NO_T = len(seqs_x)
+SEQ_NO_Q = len(seqs_y)
+
+with time.timing("Total: "):
+    d = invoke(seqs_x, seqs_y)

applications/v1.0/needleman_wunsch.codon

@@ -0,0 +1,50 @@
+import time
+import sys
+from vectron.dispatcher import *
+
+var_type = "i16"
+
+
+@vectron_cmp
+def M(x, a, b, am, y):
+    """
+    Due to ambiguous elements ('N'), a specialized match function has been used
+    instead of a ternary operator, hence the "@vectron.cmp" decorator.
+    """
+    if str(x) == "N" or str(y) == "N":
+        return am
+    elif x == y:
+        return a
+    else:
+        return b
+
+@vectron_kernel
+def nw(h, m):
+    q = [[0 if (i == 0 and j == 0) else (i * (-4)) if (j == 0 and i > 0) else (j * (-4)) if (i == 0 and j > 0) else 0 for j in range(len(m) + 1)] for i in range(len(h) + 1)]
+    for i in range(1, len(m) + 1):
+        for j in range(1, len(h) + 1):
+            q[i][j] = max(q[i - 1][j - 1] + M(m[i - 1], 2, -4, -3, h[j - 1]),
+                        q[i - 1][j] - 4, 
+                        q[i][j - 1] - 4)
+
+    return q[-1][-1]
+
+@vectron_scheduler
+def invoke(x, y):
+    score = [[0 for _ in range(len(y))] for __ in range(len(x))]
+    for i in range(len(x)):
+        for j in range(len(y)):
+            score[i][j] = nw(x[i], y[j])
+    return score
+
+with open(sys.argv[-1], 'r') as file:
+    seqs_x = [line.strip() for line in file]
+
+with open(sys.argv[-2], 'r') as file:
+    seqs_y = [line.strip() for line in file]
+
+SEQ_NO_T = len(seqs_x)
+SEQ_NO_Q = len(seqs_y)
+
+with time.timing("Total: "):
+    d = invoke(seqs_x, seqs_y)