Repository: jrenaud90/CyRK (language: python)

Issue:
Maximum system size
So I finally got around to updating my older `cyrk_ode` code to use `pysolver_ivp`, which was a very smooth process. But then I tried one of our larger test systems and discovered the new C++ backend has a different limit for the system size (32 instead of 65535). We somewhat regularly hit the old ushort limit, so dropping to 32 makes things fairly challenging. Of course, I strongly suspect there was a good reason to change the size, so I'm curious to know what appetite you have to increase that limit, and what it could be increased to? Or perhaps if the limit could be configurable at run-time without significant detriment to others with more sane system sizes?

To establish a sense of scale, we are solving quantum master equations, so the system size is `n^2-1`, where `n` is the number of states in the problem. So the scaling is abysmal. Common, simple systems (for us) have `n=4-10`, giving eqs 15-99. A system of 16 states gives 255 eqs. And we envision doing systems on the order of `n>=50` very soon (so 2499 eqs).


Discussion / hints:
wow that is a lot of differential equations!

So I have been debating what to do about this. Right now, CyRK compiles some arrays (e.g., dy; y; and arrays that depend on their size) to the stack to improve performance and avoid expensive heap allocations. However, to stack allocate I have to know the number of variables at compile time. 32 is usually overkill for my types of problems so typically I am underutilizing these allocations. Whatever choice I make, it will be set for all users of CyRK so folks who are only doing a ODE with 4 equations would be stuck with memory usage (and related cache misses) for a 65,000 ODE (it is a lot worse than this because there are several arrays that need to be allocated with this size).

The answer is to heap allocate these arrays. Let me do some testing to see how much that actually impacts performance. If it is not too bad then it will be easy to implement (will likely put it into v0.12.0 since it could affect folks' code).

**Workaround**
In the mean time, if you want to get around this limit you can do:
- ```git clone https://github.com/jrenaud90/CyRK``` to a preferred directory. 
- Edit the file CyRK/CyRK/cy/common.hpp 
  - and change:
  - "Y_LIMIT  = 65535"
  - and 
  - "DY_LIMIT    = 65535" (or larger if you are capturing [extra outputs](https://github.com/jrenaud90/CyRK/blob/main/Documentation/Extra%20Output.md))
- Go back to top level directory and edit the "pyproject.toml" file and change  "version = '0.11.3'" to something like "version = '0.11.4a0'" just so you make sure you are using the right version.
- Open a terminal and manually install:
  - `python -m pip uninstall cyrk` (get rid of old ones)
  - `python -m pip install -v .`

Let me know if that works for you (also would be curious how it affects performance).

By the way the newest version of CyRK, v0.11.3, has several performance improvements and bugfixes so I would go ahead an work off of that version from now on!

Also it is on conda now ```conda install -c conda-forge  cyrk``` (but you will likely want to do the git clone method to make your edits).

Fix patch:
diff --git a/Benchmarks/CyRK - SciPy Comparison.ipynb b/Benchmarks/CyRK - SciPy Comparison.ipynb
index 0b911b5..df61e4f 100644
--- a/Benchmarks/CyRK - SciPy Comparison.ipynb
+++ b/Benchmarks/CyRK - SciPy Comparison.ipynb
@@ -18,7 +18,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.11.3\n"
+      "0.11.4\n"
      ]
     }
    ],
@@ -609,7 +609,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -708,37 +708,37 @@
      "text": [
       "How much faster X is vs. Y\n",
       "End Time: 1.0e-03\n",
-      "\t nbsolve_ivp = 10.35x SciPy\t nbsolve_ivp =  0.21x cysolve_ivp\n",
-      "\t pysolve_ivp = 16.75x SciPy\t pysolve_ivp =  0.33x cysolve_ivp\n",
-      "\t cysolve_ivp = 50.31x SciPy\t cysolve_ivp =  3.00x pysolve_ivp\n",
+      "\t nbsolve_ivp = 10.37x SciPy\t nbsolve_ivp =  0.22x cysolve_ivp\n",
+      "\t pysolve_ivp = 16.33x SciPy\t pysolve_ivp =  0.34x cysolve_ivp\n",
+      "\t cysolve_ivp = 47.57x SciPy\t cysolve_ivp =  2.91x pysolve_ivp\n",
       "End Time: 1.0e-02\n",
-      "\t nbsolve_ivp = 10.51x SciPy\t nbsolve_ivp =  0.21x cysolve_ivp\n",
-      "\t pysolve_ivp = 16.88x SciPy\t pysolve_ivp =  0.33x cysolve_ivp\n",
-      "\t cysolve_ivp = 51.17x SciPy\t cysolve_ivp =  3.03x pysolve_ivp\n",
+      "\t nbsolve_ivp = 10.41x SciPy\t nbsolve_ivp =  0.22x cysolve_ivp\n",
+      "\t pysolve_ivp = 15.75x SciPy\t pysolve_ivp =  0.34x cysolve_ivp\n",
+      "\t cysolve_ivp = 46.80x SciPy\t cysolve_ivp =  2.97x pysolve_ivp\n",
       "End Time: 1.0e-01\n",
-      "\t nbsolve_ivp = 14.80x SciPy\t nbsolve_ivp =  0.21x cysolve_ivp\n",
-      "\t pysolve_ivp = 20.15x SciPy\t pysolve_ivp =  0.28x cysolve_ivp\n",
-      "\t cysolve_ivp = 71.54x SciPy\t cysolve_ivp =  3.55x pysolve_ivp\n",
+      "\t nbsolve_ivp = 14.76x SciPy\t nbsolve_ivp =  0.23x cysolve_ivp\n",
+      "\t pysolve_ivp = 19.68x SciPy\t pysolve_ivp =  0.30x cysolve_ivp\n",
+      "\t cysolve_ivp = 65.30x SciPy\t cysolve_ivp =  3.32x pysolve_ivp\n",
       "End Time: 1.0e+00\n",
-      "\t nbsolve_ivp = 30.43x SciPy\t nbsolve_ivp =  0.22x cysolve_ivp\n",
-      "\t pysolve_ivp = 27.62x SciPy\t pysolve_ivp =  0.20x cysolve_ivp\n",
-      "\t cysolve_ivp = 137.45x SciPy\t cysolve_ivp =  4.98x pysolve_ivp\n",
+      "\t nbsolve_ivp = 29.15x SciPy\t nbsolve_ivp =  0.25x cysolve_ivp\n",
+      "\t pysolve_ivp = 26.41x SciPy\t pysolve_ivp =  0.23x cysolve_ivp\n",
+      "\t cysolve_ivp = 115.34x SciPy\t cysolve_ivp =  4.37x pysolve_ivp\n",
       "End Time: 1.0e+01\n",
-      "\t nbsolve_ivp = 101.84x SciPy\t nbsolve_ivp =  0.25x cysolve_ivp\n",
-      "\t pysolve_ivp = 35.79x SciPy\t pysolve_ivp =  0.09x cysolve_ivp\n",
-      "\t cysolve_ivp = 407.46x SciPy\t cysolve_ivp = 11.39x pysolve_ivp\n",
+      "\t nbsolve_ivp = 103.98x SciPy\t nbsolve_ivp =  0.29x cysolve_ivp\n",
+      "\t pysolve_ivp = 38.87x SciPy\t pysolve_ivp =  0.11x cysolve_ivp\n",
+      "\t cysolve_ivp = 361.05x SciPy\t cysolve_ivp =  9.29x pysolve_ivp\n",
       "End Time: 1.0e+02\n",
-      "\t nbsolve_ivp = 135.56x SciPy\t nbsolve_ivp =  0.27x cysolve_ivp\n",
-      "\t pysolve_ivp = 41.78x SciPy\t pysolve_ivp =  0.08x cysolve_ivp\n",
-      "\t cysolve_ivp = 495.33x SciPy\t cysolve_ivp = 11.86x pysolve_ivp\n",
+      "\t nbsolve_ivp = 129.63x SciPy\t nbsolve_ivp =  0.29x cysolve_ivp\n",
+      "\t pysolve_ivp = 38.85x SciPy\t pysolve_ivp =  0.09x cysolve_ivp\n",
+      "\t cysolve_ivp = 447.00x SciPy\t cysolve_ivp = 11.51x pysolve_ivp\n",
       "End Time: 1.0e+03\n",
-      "\t nbsolve_ivp = 134.88x SciPy\t nbsolve_ivp =  0.27x cysolve_ivp\n",
-      "\t pysolve_ivp = 40.29x SciPy\t pysolve_ivp =  0.08x cysolve_ivp\n",
-      "\t cysolve_ivp = 502.51x SciPy\t cysolve_ivp = 12.47x pysolve_ivp\n",
+      "\t nbsolve_ivp = 133.10x SciPy\t nbsolve_ivp =  0.29x cysolve_ivp\n",
+      "\t pysolve_ivp = 38.65x SciPy\t pysolve_ivp =  0.08x cysolve_ivp\n",
+      "\t cysolve_ivp = 460.74x SciPy\t cysolve_ivp = 11.92x pysolve_ivp\n",
       "End Time: 1.0e+04\n",
-      "\t nbsolve_ivp = 116.16x SciPy\t nbsolve_ivp =  0.25x cysolve_ivp\n",
-      "\t pysolve_ivp = 39.70x SciPy\t pysolve_ivp =  0.09x cysolve_ivp\n",
-      "\t cysolve_ivp = 465.39x SciPy\t cysolve_ivp = 11.72x pysolve_ivp\n"
+      "\t nbsolve_ivp = 110.75x SciPy\t nbsolve_ivp =  0.25x cysolve_ivp\n",
+      "\t pysolve_ivp = 38.30x SciPy\t pysolve_ivp =  0.09x cysolve_ivp\n",
+      "\t cysolve_ivp = 442.66x SciPy\t cysolve_ivp = 11.56x pysolve_ivp\n"
      ]
     }
    ],
diff --git a/Benchmarks/CyRK_CySolver.pdf b/Benchmarks/CyRK_CySolver.pdf
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diff --git a/Benchmarks/CyRK_CySolver_DenseOn.pdf b/Benchmarks/CyRK_CySolver_DenseOn.pdf
index e62df6d..d9c1fd7 100644
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diff --git a/Benchmarks/CyRK_PySolver (njit).pdf b/Benchmarks/CyRK_PySolver (njit).pdf
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diff --git a/Benchmarks/CyRK_SciPy_Compare_predprey_v0-11-4.png b/Benchmarks/CyRK_SciPy_Compare_predprey_v0-11-4.png
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index 0000000..600b8b5
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diff --git a/Benchmarks/CyRK_numba.pdf b/Benchmarks/CyRK_numba.pdf
index 8485806..92bb367 100644
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diff --git a/Benchmarks/SciPy.pdf b/Benchmarks/SciPy.pdf
index 5184586..d544d3a 100644
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diff --git a/Benchmarks/CyRK_SciPy_Compare_predprey_v0-11-3.png b/Benchmarks/archive/CyRK_SciPy_Compare_predprey_v0-11-3.png
similarity index 100%
rename from Benchmarks/CyRK_SciPy_Compare_predprey_v0-11-3.png
rename to Benchmarks/archive/CyRK_SciPy_Compare_predprey_v0-11-3.png
diff --git a/CHANGES.md b/CHANGES.md
index 4ac2cad..f54d79c 100644
--- a/CHANGES.md
+++ b/CHANGES.md
@@ -2,6 +2,11 @@
 
 ## 2024
 
+#### v0.11.4 (2024-11-25)
+
+C++ Changes
+* Moved away from stack to heap allocated vectors for arrays that depend on the size of y0. Prior stack allocation required a hard upper limit which was a limitation for some use cases (See GitHub Issue [#74](https://github.com/jrenaud90/CyRK/issues/74)).
+
 #### v0.11.3 (2024-11-18)
 
 New:
@@ -43,7 +48,7 @@ Fixes:
 Issues:
 * Cython `prange` was failing (for the array interps) only on MacOS on some versions of Python. Couldn't figure it out so removing prange for now.
 
-#### v0.11.0 (2024-11-09)
+### v0.11.0 (2024-11-09)
 
 New:
 * `WrapCySolverResult` result class now provides user access to attribute `num_y`.
diff --git a/CyRK/cy/common.hpp b/CyRK/cy/common.hpp
index 311569a..4f259d7 100644
--- a/CyRK/cy/common.hpp
+++ b/CyRK/cy/common.hpp
@@ -4,8 +4,6 @@
 #include <limits>
 
 // Pre-processor constants
-static const size_t Y_LIMIT     = 32;
-static const size_t DY_LIMIT    = 64;  // dy limit is defined by Y_LIMIT and number of extra output allowed. Typically we allow 2x the Y_LIMIT.
 static const int MESSAGE_SIZE   = 128;
 static const size_t BUFFER_SIZE = 16;
 
diff --git a/CyRK/cy/cysolution.cpp b/CyRK/cy/cysolution.cpp
index aa5f639..a7a1336 100644
--- a/CyRK/cy/cysolution.cpp
+++ b/CyRK/cy/cysolution.cpp
@@ -107,30 +107,6 @@ void CySolverResult::p_expand_data_storage()
     }
 }
 
-void CySolverResult::p_offload_data()
-{
-    /*
-    * Saves integration data that were temporarily held in buffers to heap-allocated vectors.
-    */
-
-    this->size += this->current_data_buffer_size;
-
-    if (this->size > this->storage_capacity)
-    {
-        // There is not enough room in the storage vectors. Expand them.
-        this->p_expand_data_storage();
-    }
-
-    // Save time results
-    this->time_domain_vec.insert(this->time_domain_vec.end(), this->data_buffer_time_ptr, this->data_buffer_time_ptr + this->current_data_buffer_size);
-
-    // Save y results and any extra output
-    this->solution.insert(this->solution.end(), this->data_buffer_y_ptr, this->data_buffer_y_ptr + (this->num_dy * this->current_data_buffer_size));
-
-    // Reset buffers
-    this->current_data_buffer_size = 0;
-}
-
 
 // Public methods
 void CySolverResult::set_expected_size(size_t expected_size)
@@ -156,9 +132,9 @@ void CySolverResult::reset()
     // Set the storage size to the original expected size.
     this->storage_capacity = this->original_expected_size;
 
-    // Reset buffer trackers
-    this->current_data_buffer_size  = 0;
-    this->num_interpolates          = 0;
+    // Reset trackers
+    this->size = 0;
+    this->num_interpolates = 0;
 
     // Reserve the memory for the vectors
     try
@@ -283,27 +259,25 @@ void CySolverResult::reset_solver()
 
 void CySolverResult::save_data(const double new_t, double* const new_solution_y_ptr, double* const new_solution_dy_ptr)
 {
-    // Check if our data buffer is full
-    if (this->current_data_buffer_size >= BUFFER_SIZE)
+    this->size++;
+    if (this->size > this->storage_capacity)
     {
-        this->p_offload_data();
+        // There is not enough room in the storage vectors. Expand them.
+        this->p_expand_data_storage();
     }
 
-    // Save data in buffer
-    // Save time
-    this->data_buffer_time_ptr[this->current_data_buffer_size] = new_t;
+    // Save time results
+    this->time_domain_vec.push_back(new_t);
 
-    // Save y
-    size_t stride = this->current_data_buffer_size * this->num_dy;  // We have to stride across num_dy even though we are only saving num_y values.
-    std::memcpy(&this->data_buffer_y_ptr[stride], new_solution_y_ptr, sizeof(double) * this->num_y);
+    // Save y results
+    this->solution.insert(this->solution.end(), new_solution_y_ptr, new_solution_y_ptr + this->num_y);
 
-    // Save extra
-    if (this->num_extra > 0)
+    if (this->capture_extra)
     {
-        std::memcpy(&this->data_buffer_y_ptr[stride + this->num_y], &new_solution_dy_ptr[this->num_y], sizeof(double) * this->num_extra);
+        // Save extra ouput results
+        // Start at the end of y values (Dependent dys) and go to the end of the dy array
+        this->solution.insert(this->solution.end(), &new_solution_dy_ptr[this->num_y], new_solution_dy_ptr + this->num_dy);
     }
-
-    this->current_data_buffer_size++;
 }
 
 int CySolverResult::build_dense(bool save)
@@ -367,12 +341,6 @@ void CySolverResult::solve()
 
 void CySolverResult::finalize()
 {
-    // Offload anything in the buffer
-    if (this->current_data_buffer_size > 0)
-    {
-        this->p_offload_data();
-    }
-
     // Shrink vectors
     if (this->size > 100000)
     {
diff --git a/CyRK/cy/cysolution.hpp b/CyRK/cy/cysolution.hpp
index fa6c633..ea47642 100644
--- a/CyRK/cy/cysolution.hpp
+++ b/CyRK/cy/cysolution.hpp
@@ -21,11 +21,6 @@ class CySolverResult : public std::enable_shared_from_this<CySolverResult>{
     // Current storage information
     size_t original_expected_size = 0;
     size_t storage_capacity       = 0;
-
-    // Buffer
-    size_t current_data_buffer_size  = 0;
-    double* data_buffer_time_ptr     = &data_buffer_time[0];
-    double* data_buffer_y_ptr        = &data_buffer_y[0];
     
     // Metadata
     double last_t     = 0;
@@ -84,16 +79,11 @@ class CySolverResult : public std::enable_shared_from_this<CySolverResult>{
     std::vector<double> interp_time_vec = std::vector<double>();
 
 private:
-    // Put data buffers at the end of memory stack
-    double data_buffer_time[BUFFER_SIZE]                 = { };
-    double data_buffer_y[BUFFER_SIZE * DY_LIMIT]         = { };
-
 
 // Methods
 protected:
     void p_delete_heap();
     void p_expand_data_storage();
-    void p_offload_data();
 
 public:
     virtual ~CySolverResult();
diff --git a/CyRK/cy/cysolver.cpp b/CyRK/cy/cysolver.cpp
index 188a423..3efdc98 100644
--- a/CyRK/cy/cysolver.cpp
+++ b/CyRK/cy/cysolver.cpp
@@ -61,20 +61,7 @@ CySolverBase::CySolverBase(
     this->storage_sptr->update_message("CySolverBase Initializing.");
 
     // Check for errors
-    if (this->num_extra > (DY_LIMIT - Y_LIMIT))
-    {
-        this->status = -9;
-        this->storage_sptr->error_code = -1;
-        this->storage_sptr->update_message("CySolverBase Attribute Error: `num_extra` exceeds the maximum supported size.");
-    }
-
-    if (this->num_y > Y_LIMIT)
-    {
-        this->status = -9;
-        this->storage_sptr->error_code = -1;
-        this->storage_sptr->update_message("CySolverBase Attribute Error: `num_y` exceeds the maximum supported size.");
-    }
-    else if (this->num_y == 0)
+    if (this->num_y == 0)
     {
         this->status = -9;
         this->storage_sptr->error_code = -1;
@@ -85,6 +72,18 @@ CySolverBase::CySolverBase(
     this->num_y_dbl  = (double)this->num_y;
     this->num_y_sqrt = std::sqrt(this->num_y_dbl);
     this->num_dy     = this->num_y + this->num_extra;
+
+    // Set up heap allocated arrays
+    this->y0.resize(this->num_y);
+    this->y_old.resize(this->num_y);
+    this->y_now.resize(this->num_y);
+    this->y_tmp.resize(this->num_y);
+    this->y_interp.resize(this->num_y);
+    // For dy, both the dy/dt and any extra outputs are stored. So the maximum size is `num_y` (25) + `num_extra` (25)
+    this->dy_old.resize(this->num_dy);
+    this->dy_now.resize(this->num_dy);
+    this->dy_tmp.resize(this->num_dy);
+
     // Make a copy of y0
     std::memcpy(&this->y0[0], y0_ptr, sizeof(double) * this->num_y);
     
@@ -200,7 +199,7 @@ void CySolverBase::calc_first_step_size()
 
 NowStatePointers CySolverBase::get_now_state()
 {
-    return NowStatePointers(&this->t_now, &this->y_now[0], &this->dy_now[0]);
+    return NowStatePointers(&this->t_now, this->y_now.data(), this->dy_now.data());
 }
 
 bool CySolverBase::check_status() const
@@ -232,7 +231,7 @@ bool CySolverBase::check_status() const
 void CySolverBase::cy_diffeq() noexcept
 {
     // Call c function
-    this->diffeq_ptr(&this->dy_now[0], this->t_now, &this->y_now[0], this->args_ptr, this->pre_eval_func);
+    this->diffeq_ptr(this->dy_now.data(), this->t_now, this->y_now.data(), this->args_ptr, this->pre_eval_func);
 }
 
 void CySolverBase::reset()
@@ -400,8 +399,6 @@ void CySolverBase::take_step()
                     // There are steps we need to interpolate over.
                     // Start with the old time and add t_eval step sizes until we are done.
                     // Create a y array and dy_array to use during interpolation
-                    double y_interp[Y_LIMIT] = { };
-                    double* y_interp_ptr     = &y_interp[0];
 
                     // If capture extra is set to true then we need to hold onto a copy of the current state
                     // The current state pointers must be overwritten if extra output is to be captured.
@@ -432,7 +429,7 @@ void CySolverBase::take_step()
                         }
 
                         // Call the interpolator using this new time value.
-                        this->storage_sptr->dense_vec.back().call(t_interp, y_interp_ptr);
+                        this->storage_sptr->dense_vec.back().call(t_interp, this->y_interp.data());
 
                         if (this->capture_extra)
                         {
@@ -443,13 +440,13 @@ void CySolverBase::take_step()
 
                             // Copy the interpreted y onto the current y_now_ptr. Also update t_now
                             this->t_now = t_interp;
-                            std::memcpy(&this->y_now[0], y_interp_ptr, sizeof(double) * this->num_y);
+                            std::memcpy(&this->y_now[0], this->y_interp.data(), sizeof(double) * this->num_y);
 
                             // Call diffeq to update dy_now_ptr with the extra output.
                             this->diffeq(this);
                         }
                         // Save interpolated data to storage. If capture extra is true then dy_now holds those extra values. If it is false then it won't hurt to pass dy_now to storage.
-                        this->storage_sptr->save_data(t_interp, y_interp_ptr, &this->dy_now[0]);
+                        this->storage_sptr->save_data(t_interp, this->y_interp.data(), &this->dy_now[0]);
                     }
                 }
                 // Update the old index for the next step
diff --git a/CyRK/cy/cysolver.hpp b/CyRK/cy/cysolver.hpp
index 4ba863b..fb4a6fa 100644
--- a/CyRK/cy/cysolver.hpp
+++ b/CyRK/cy/cysolver.hpp
@@ -145,14 +145,15 @@ class CySolverBase {
     size_t num_extra = 0;
 
     // The size of the stack allocated tracking arrays is equal to the maximum allowed `num_y` (25).
-    double y0[Y_LIMIT]    = { 0.0 };
-    double y_old[Y_LIMIT] = { 0.0 };
-    double y_now[Y_LIMIT] = { 0.0 };
-    double y_tmp[Y_LIMIT] = { 0.0 };
+    std::vector<double> y0       = std::vector<double>();
+    std::vector<double> y_old    = std::vector<double>();
+    std::vector<double> y_now    = std::vector<double>();
+    std::vector<double> y_tmp    = std::vector<double>();
+    std::vector<double> y_interp = std::vector<double>();
     // For dy, both the dy/dt and any extra outputs are stored. So the maximum size is `num_y` (25) + `num_extra` (25)
-    double dy_old[DY_LIMIT] = { 0.0 };
-    double dy_now[DY_LIMIT] = { 0.0 };
-    double dy_tmp[DY_LIMIT] = { 0.0 };
+    std::vector<double> dy_old = std::vector<double>();
+    std::vector<double> dy_now = std::vector<double>();
+    std::vector<double> dy_tmp = std::vector<double>();
 
     // Result storage
     std::shared_ptr<CySolverResult> storage_sptr = nullptr;
diff --git a/CyRK/cy/cysolver_api.pxd b/CyRK/cy/cysolver_api.pxd
index e553525..4d07dd5 100644
--- a/CyRK/cy/cysolver_api.pxd
+++ b/CyRK/cy/cysolver_api.pxd
@@ -15,8 +15,6 @@ cnp.import_array()
 cdef extern from "common.cpp" nogil:
     const double INF
     const double EPS_100
-    const size_t Y_LIMIT
-    const size_t DY_LIMIT
     const size_t BUFFER_SIZE
     const double MAX_STEP
 
@@ -179,8 +177,8 @@ cdef extern from "cysolver.cpp" nogil:
         shared_ptr[CySolverResult] storage_ptr
         size_t len_t
         double t_now
-        double* y_now_ptr
-        double* dy_now_ptr
+        vector[double] y_now
+        vector[double] dy_now
 
         cpp_bool check_status()
         NowStatePointers get_now_state()
diff --git a/CyRK/cy/pysolver.pyx b/CyRK/cy/pysolver.pyx
index 84c2958..9fd81bf 100644
--- a/CyRK/cy/pysolver.pyx
+++ b/CyRK/cy/pysolver.pyx
@@ -5,7 +5,7 @@ from libc.string cimport memcpy
 from libcpp.cmath cimport fmin, fabs
 
 from CyRK.utils.memory cimport make_shared
-from CyRK.cy.cysolver_api cimport find_expected_size, WrapCySolverResult, INF, EPS_100, Y_LIMIT, DY_LIMIT
+from CyRK.cy.cysolver_api cimport find_expected_size, WrapCySolverResult, INF, EPS_100
 
 import numpy as np
 cimport numpy as cnp
@@ -137,10 +137,6 @@ def pysolve_ivp(
     # Parse y0
     cdef size_t num_y   = len(y0)
     cdef const double* y0_ptr = &y0[0]
-    if num_y > Y_LIMIT:
-        raise AttributeError(
-            f"CyRK only supports a maximum number of {Y_LIMIT} dependent variables. {num_y} were provided."
-            )
     
     # Parse t_eval
     cdef const double* t_eval_ptr = NULL
@@ -152,10 +148,6 @@ def pysolve_ivp(
         t_eval_ptr = &t_eval[0]
     
     # Parse num_extra
-    if num_extra > (DY_LIMIT - Y_LIMIT):
-        raise AttributeError(
-            f"CyRK can only capture a maximum number of {DY_LIMIT - Y_LIMIT} extra outputs. {num_extra} were provided."
-            )
     cdef size_t num_dy = num_y + num_extra
     
     # Parse rtol
@@ -257,7 +249,7 @@ def pysolve_ivp(
         )
 
     # Get pointers to the solver so that the Python differential equation can use and update its state variables (t_now, y_now, dy_now).
-    cdef NowStatePointers solver_state = solver.solution_sptr.get().solver_uptr.get().get_now_state()
+    cdef NowStatePointers solver_state = solution_sptr.get().solver_uptr.get().get_now_state()
     diffeq_wrap.set_state(&solver_state)
     
     ##
diff --git a/CyRK/cy/rk.cpp b/CyRK/cy/rk.cpp
index 56e27ab..8cb9492 100644
--- a/CyRK/cy/rk.cpp
+++ b/CyRK/cy/rk.cpp
@@ -70,6 +70,8 @@ RKSolver::RKSolver(
     {
         // rtol for each y
         this->use_array_rtols = true;
+        // Allocate array
+        this->rtols.resize(this->num_y);
         for (size_t y_i = 0; y_i < this->num_y; y_i++)
         {
             double temp_double = rtols_ptr[y_i];
@@ -77,29 +79,36 @@ RKSolver::RKSolver(
             {
                 temp_double = EPS_100;
             }
-            this->rtols_ptr[y_i] = temp_double;
+            this->rtols[y_i] = temp_double;
         }
     }
     else {
         // only one rtol
         double temp_double = rtol;
+        this->rtols.resize(1);
         if (temp_double < EPS_100) [[unlikely]]
         {
             temp_double = EPS_100;
         }
-        this->rtols_ptr[0] = temp_double;
+        this->rtols[0] = temp_double;
     }
 
     if (atols_ptr)
     {
         // atol for each y
         this->use_array_atols = true;
-        std::memcpy(this->atols_ptr, atols_ptr, sizeof(double) * this->num_y);
+        this->atols.resize(this->num_y);
+        std::memcpy(this->atols.data(), atols_ptr, sizeof(double) * this->num_y);
     }
     else {
         // only one atol
-        this->atols_ptr[0] = atol;
+        this->atols.resize(1);
+        this->atols[0] = atol;
     }
+
+    // Setup rtol and atol pointers
+    this->rtols_ptr = this->rtols.data();
+    this->atols_ptr = this->atols.data();
 }
 
 
@@ -197,10 +206,10 @@ void RKSolver::p_step_implementation()
     const double* const l_A_ptr = this->A_ptr;
     const double* const l_B_ptr = this->B_ptr;
     const double* const l_C_ptr = this->C_ptr;
-    double* const l_y_now_ptr   = &this->y_now[0];
-    double* const l_y_old_ptr   = &this->y_old[0];
-    double* const l_dy_now_ptr  = &this->dy_now[0];
-    double* const l_dy_old_ptr  = &this->dy_old[0];
+    double* const l_y_now_ptr   = this->y_now.data();
+    double* const l_y_old_ptr   = this->y_old.data();
+    double* const l_dy_now_ptr  = this->dy_now.data();
+    double* const l_dy_old_ptr  = this->dy_old.data();
 
     // Determine step size based on previous loop
     // Find minimum step size based on the value of t (less floating point numbers between numbers when t is large)
@@ -823,12 +832,11 @@ void RKSolver::set_Q_array(double* Q_ptr)
         this->offload_to_temp();
 
         // We also need to store dy_dt so that they can be used in dot products
-        double K_extended[Y_LIMIT * 3] = { };
-        double* K_extended_ptr = &K_extended[0];
+        double* K_extended_ptr        = &this->K[13 * this->num_y];
+        double* temp_double_array_ptr = &this->K[16 * this->num_y];
 
         // S (row) == 13
         // Solve for dy used to call diffeq
-        double temp_double_arr[Y_LIMIT * 2] = { };
         double temp_double;
         double temp_double_2;
         double temp_double_3;
@@ -842,14 +850,14 @@ void RKSolver::set_Q_array(double* Q_ptr)
             stride_K = this->n_stages_p1 * y_i;
             // Go up to a max of Row 13
             temp_double   = 0.0;
-            temp_double_arr[y_i * 2]     = 0.0;
-            temp_double_arr[y_i * 2 + 1] = 0.0;
+            temp_double_array_ptr[y_i * 2]     = 0.0;
+            temp_double_array_ptr[y_i * 2 + 1] = 0.0;
             for (size_t n_i = 0; n_i < this->n_stages_p1; n_i++)
             {
                 K_ni = K_ptr[stride_K + n_i];
                 temp_double                  += K_ni * DOP853_AEXTRA_ptr[3 * n_i];
-                temp_double_arr[y_i * 2]     += K_ni * DOP853_AEXTRA_ptr[3 * n_i + 1];
-                temp_double_arr[y_i * 2 + 1] += K_ni * DOP853_AEXTRA_ptr[3 * n_i + 2];
+                temp_double_array_ptr[y_i * 2]     += K_ni * DOP853_AEXTRA_ptr[3 * n_i + 1];
+                temp_double_array_ptr[y_i * 2 + 1] += K_ni * DOP853_AEXTRA_ptr[3 * n_i + 2];
             }
 
             // Update y for diffeq call using the temp_double dot product.
@@ -868,11 +876,11 @@ void RKSolver::set_Q_array(double* Q_ptr)
 
             // Dot Product (K.T dot a) * h
             // Add row 14 to the remaining dot product trackers
-            temp_double_arr[y_i * 2]     += K_ni * DOP853_AEXTRA_ptr[3 * 13 + 1];
-            temp_double_arr[y_i * 2 + 1] += K_ni * DOP853_AEXTRA_ptr[3 * 13 + 2];
+            temp_double_array_ptr[y_i * 2]     += K_ni * DOP853_AEXTRA_ptr[3 * 13 + 1];
+            temp_double_array_ptr[y_i * 2 + 1] += K_ni * DOP853_AEXTRA_ptr[3 * 13 + 2];
 
             // Update y for diffeq call
-            this->y_now[y_i] = this->y_old[y_i] + temp_double_arr[y_i * 2] * this->step;
+            this->y_now[y_i] = this->y_old[y_i] + temp_double_array_ptr[y_i * 2] * this->step;
         }
         // Update time and call the diffeq.
         this->t_now = this->t_old + (this->step * DOP853_CEXTRA_ptr[1]);
@@ -887,10 +895,10 @@ void RKSolver::set_Q_array(double* Q_ptr)
 
             // Dot Product (K.T dot a) * h            
             // Add row 15 to the remaining dot product trackers
-            temp_double_arr[y_i * 2 + 1] += K_ni * DOP853_AEXTRA_ptr[3 * 14 + 2];
+            temp_double_array_ptr[y_i * 2 + 1] += K_ni * DOP853_AEXTRA_ptr[3 * 14 + 2];
 
             // Update y for diffeq call
-            this->y_now[y_i] = this->y_old[y_i] + temp_double_arr[y_i * 2 + 1] * this->step;
+            this->y_now[y_i] = this->y_old[y_i] + temp_double_array_ptr[y_i * 2 + 1] * this->step;
         }
         // Update time and call the diffeq.
         this->t_now = this->t_old + (this->step * DOP853_CEXTRA_ptr[2]);
@@ -1016,6 +1024,10 @@ void RKSolver::set_Q_array(double* Q_ptr)
 // ########################################################################################################################
 void RK23::reset()
 {
+
+    // Allocate the size of K
+    this->K.resize(this->num_y * 4);
+
     // Setup RK constants before calling the base class reset
     this->C_ptr     = RK23_C_ptr;
     this->A_ptr     = RK23_A_ptr;
@@ -1025,7 +1037,7 @@ void RK23::reset()
     this->E5_ptr    = nullptr;       // Not used for RK23
     this->P_ptr     = RK23_P_ptr;       
     this->D_ptr     = nullptr;       // Not used for RK23
-    this->K_ptr     = &this->K[0];
+    this->K_ptr     = this->K.data();
     this->order     = RK23_order;
     this->n_stages  = RK23_n_stages;
     this->len_Acols = RK23_len_Acols;
@@ -1044,6 +1056,9 @@ void RK23::reset()
 // ########################################################################################################################
 void RK45::reset()
 {
+    // Allocate the size of K
+    this->K.resize(this->num_y * 7);
+
     // Setup RK constants before calling the base class reset
     this->C_ptr     = RK45_C_ptr;
     this->A_ptr     = RK45_A_ptr;
@@ -1053,7 +1068,7 @@ void RK45::reset()
     this->E5_ptr    = nullptr;       // Not used for RK45
     this->P_ptr     = RK45_P_ptr;
     this->D_ptr     = nullptr;       // Not used for RK45
-    this->K_ptr     = &this->K[0];
+    this->K_ptr     = this->K.data();
     this->order     = RK45_order;
     this->n_stages  = RK45_n_stages;
     this->len_Acols = RK45_len_Acols;
@@ -1072,6 +1087,12 @@ void RK45::reset()
 // ########################################################################################################################
 void DOP853::reset()
 {
+    // Allocate the size of K
+    this->K.resize(this->num_y * 18);
+        // First 13 cols are K
+        // Next 3 are K_extended
+        // Next 2 are temp_double_array_ptr
+
     // Setup RK constants before calling the base class reset
     this->C_ptr     = DOP853_C_ptr;
     this->A_ptr     = DOP853_A_ptr;
@@ -1081,7 +1102,7 @@ void DOP853::reset()
     this->E5_ptr    = DOP853_E5_ptr;
     this->P_ptr     = nullptr;        // TODO: Not implemented
     this->D_ptr     = nullptr;        // TODO: Not implemented
-    this->K_ptr     = &this->K[0];
+    this->K_ptr     = this->K.data();
     this->order     = DOP853_order;
     this->n_stages  = DOP853_n_stages;
     this->len_Acols = DOP853_A_cols;
diff --git a/CyRK/cy/rk.hpp b/CyRK/cy/rk.hpp
index 6ad5390..8add821 100644
--- a/CyRK/cy/rk.hpp
+++ b/CyRK/cy/rk.hpp
@@ -1,6 +1,6 @@
 #pragma once
 
-
+#include <vector>
 #include "common.hpp"
 #include "cysolver.hpp"
 
@@ -658,13 +658,13 @@ class RKSolver : public CySolverBase {
     // RK constants
     size_t order = 0;
     size_t error_estimator_order = 0;
-    size_t n_stages     = 0;
-    size_t n_stages_p1  = 0;
-    size_t len_Acols    = 0;
-    size_t len_C        = 0;
-    size_t len_Pcols    = 0;
-    size_t nstages_numy = 0;
-    double error_exponent     = 0.0;
+    size_t n_stages       = 0;
+    size_t n_stages_p1    = 0;
+    size_t len_Acols      = 0;
+    size_t len_C          = 0;
+    size_t len_Pcols      = 0;
+    size_t nstages_numy   = 0;
+    double error_exponent = 0.0;
 
     // Pointers to RK constant arrays
     const double* C_ptr  = nullptr;
@@ -675,19 +675,19 @@ class RKSolver : public CySolverBase {
     const double* E5_ptr = nullptr;
     const double* P_ptr  = nullptr;
     const double* D_ptr  = nullptr;
+    double* K_ptr        = nullptr;
 
     // K is not const. Its values are stored in an array that is held by this class.
-    double K[1]   = { std::nan("") };
-    double* K_ptr = &this->K[0];
+    std::vector<double> K = std::vector<double>();
 
     // Tolerances
     // For the same reason num_y is limited, the total number of tolerances are limited.
-    double rtols[Y_LIMIT] = { std::nan("") };
-    double atols[Y_LIMIT] = { std::nan("") };
-    double* rtols_ptr     = &rtols[0];
-    double* atols_ptr     = &atols[0];
-    bool use_array_rtols  = false;
-    bool use_array_atols  = false;
+    std::vector<double> rtols = std::vector<double>();
+    std::vector<double> atols = std::vector<double>();
+    double* rtols_ptr = nullptr;
+    double* atols_ptr = nullptr;
+    bool use_array_rtols = false;
+    bool use_array_atols = false;
 
     // Step size parameters
     double user_provided_first_step_size = 0.0;
@@ -743,7 +743,6 @@ class RKSolver : public CySolverBase {
 class RK23 : public RKSolver {
 
 protected:
-    double K[4 * Y_LIMIT] = { 0.0 };
 
 public:
     // Copy over base class constructors
@@ -754,7 +753,6 @@ class RK23 : public RKSolver {
 class RK45 : public RKSolver {
 
 protected:
-    double K[7 * Y_LIMIT] = { 0.0 };
 
 public:
     // Copy over base class constructors
@@ -765,7 +763,9 @@ class RK45 : public RKSolver {
 class DOP853 : public RKSolver {
 
 protected:
-    double K[13 * Y_LIMIT] = { 0.0 };
+
+    std::vector<double> K_extended      = std::vector<double>();
+    std::vector<double> temp_double_arr = std::vector<double>();
 
 public:
     // Copy over base class constructors
diff --git a/Performance/cyrk_performance-DOP853.csv b/Performance/cyrk_performance-DOP853.csv
index 7afd8a0..54fb50b 100644
--- a/Performance/cyrk_performance-DOP853.csv
+++ b/Performance/cyrk_performance-DOP853.csv
@@ -25,3 +25,4 @@ CyRK Version, Run-on Date, cython (avg), cython (std),CySolver (avg),CySolver (s
 0.11.0, 08/11/2024 13:34:31, 1.0405, 0.0220, 0.0440, 0.0004, 0.1653, 0.0037, 10.6848, 0.0518, 0.4168, 0.0055, 1.5772, 0.0952, 0.4697, 0.0056, 0.0350, 0.0010, 0.1027, 0.0017, 4.5382, 0.0695, 0.2962, 0.0080, 0.8286, 0.0180, 1.5190, 0.0184, 0.0886, 0.0108, 0.3325, 0.1084, 21.0697, 0.8173, 1.0653, 0.0440, 3.3296, 0.0833, 1.7694, 0.0377, 0.0796, 0.0011, 0.5066, 0.0051, 24.3005, 0.1841, 1.0867, 0.0503, 6.9565, 0.2617
 0.11.2, 13/11/2024 09:47:14, 1.0383, 0.0482, 0.0459, 0.0011, 0.1791, 0.0065, 10.4672, 0.3348, 0.4292, 0.0054, 1.5538, 0.0164, 0.4595, 0.0036, 0.0361, 0.0011, 0.1058, 0.0015, 4.6616, 0.1080, 0.3028, 0.0064, 0.8637, 0.0168, 1.4801, 0.0422, 0.0815, 0.0003, 0.2740, 0.0060, 20.1617, 0.3500, 1.0886, 0.0310, 3.4030, 0.0480, 1.7564, 0.0416, 0.0840, 0.0008, 0.5297, 0.0179, 23.9814, 0.4823, 1.1206, 0.0153, 6.7479, 0.0770
 0.11.3, 18/11/2024 22:01:05, 1.0395, 0.0643, 0.0446, 0.0007, 0.1775, 0.0072, 10.4019, 0.4997, 0.4034, 0.0051, 1.5558, 0.0211, 0.4460, 0.0060, 0.0335, 0.0003, 0.1022, 0.0008, 4.4008, 0.0505, 0.2929, 0.0032, 0.8257, 0.0232, 1.4250, 0.0155, 0.0852, 0.0005, 0.2547, 0.0017, 20.6687, 0.6711, 1.1036, 0.0036, 3.4398, 0.3229, 1.6677, 0.0198, 0.0893, 0.0052, 0.5148, 0.0099, 25.7176, 2.5321, 1.1436, 0.0118, 6.8288, 0.1691
+0.11.4, 25/11/2024 13:48:29, 0.9518, 0.0095, 0.0469, 0.0005, 0.1849, 0.0158, 9.7986, 0.2271, 0.4914, 0.0471, 1.5895, 0.0413, 0.4717, 0.0174, 0.0371, 0.0021, 0.1015, 0.0013, 4.3503, 0.0397, 0.3076, 0.0072, 0.8205, 0.0108, 1.4690, 0.0353, 0.0912, 0.0009, 0.2552, 0.0035, 19.6132, 0.1880, 1.2057, 0.0062, 3.2306, 0.0464, 1.6630, 0.0246, 0.0927, 0.0004, 0.4967, 0.0064, 22.7714, 0.2059, 1.2428, 0.0109, 6.6800, 0.1388
diff --git a/Performance/cyrk_performance-RK23.csv b/Performance/cyrk_performance-RK23.csv
index 04759c6..cf9f73e 100644
--- a/Performance/cyrk_performance-RK23.csv
+++ b/Performance/cyrk_performance-RK23.csv
@@ -25,3 +25,4 @@ CyRK Version, Run-on Date, cython (avg), cython (std),CySolver (avg),CySolver (s
 0.11.0, 08/11/2024 13:32:05, 5.2024, 0.4024, 0.2878, 0.0104, 0.8329, 0.0146, 48.9279, 1.0915, 2.8651, 0.0911, 9.6346, 0.4435, 3.0034, 0.0548, 0.2512, 0.0073, 0.6391, 0.0091, 30.1886, 1.3712, 2.5126, 0.0594, 6.1286, 0.1974, 5.0243, 0.0111, 0.3568, 0.0029, 0.9418, 0.0134, 81.3716, 0.9542, 6.0864, 0.2366, 21.4152, 0.6340, 5.8757, 0.0795, 0.3821, 0.0257, 1511.3296, 2614.5716, 103.0198, 4.8289, 6.7153, 0.1536, 40.7782, 2.6646
 0.11.2, 13/11/2024 09:44:47, 58.5798, 92.5131, 0.3027, 0.0073, 0.9652, 0.0611, 50.2554, 3.1065, 3.0509, 0.1734, 11.0730, 0.5532, 3.0001, 0.0297, 0.2962, 0.0300, 0.6303, 0.0107, 29.3821, 0.6157, 2.4788, 0.0442, 8.6115, 2.1469, 54.1616, 85.1339, 0.3808, 0.0043, 1.1505, 0.0435, 83.2819, 0.6580, 6.1863, 0.0625, 22.4879, 0.1939, 59.9757, 93.5368, 0.3924, 0.0084, 1633.8436, 2826.6269, 105.4013, 5.7465, 7.0494, 0.0526, 41.1053, 0.3990
 0.11.3, 18/11/2024 21:58:42, 53.8381, 85.1332, 0.2844, 0.0053, 0.8357, 0.0040, 44.6139, 0.5691, 2.7175, 0.0170, 9.6441, 0.2775, 2.8966, 0.0745, 0.2544, 0.0105, 0.6118, 0.0154, 27.6303, 0.0867, 2.4112, 0.0748, 6.5003, 0.2024, 4.8056, 0.0730, 0.3556, 0.0063, 0.9397, 0.0079, 78.8993, 0.7616, 5.7215, 0.0496, 20.2977, 0.1102, 5.7503, 0.0931, 0.3808, 0.0202, 1430.3917, 2474.4326, 96.3635, 0.9127, 6.3773, 0.0910, 39.3835, 1.9905
+0.11.4, 25/11/2024 13:46:04, 53.6135, 84.9470, 0.2950, 0.0027, 0.8886, 0.0461, 47.7257, 2.7085, 3.0456, 0.1566, 10.3188, 0.4075, 2.8431, 0.0316, 0.2536, 0.0007, 0.6429, 0.0073, 29.1347, 0.7174, 2.5346, 0.0408, 6.6766, 0.1161, 5.1020, 0.1334, 0.3688, 0.0044, 0.9965, 0.0316, 84.4449, 2.4298, 6.4677, 0.2837, 23.8316, 1.0627, 55.1419, 85.4437, 0.3982, 0.0071, 1543.6904, 2670.4149, 101.9026, 1.5866, 6.8547, 0.1089, 36.6832, 0.7182
diff --git a/Performance/cyrk_performance-RK45.csv b/Performance/cyrk_performance-RK45.csv
index 344b4f2..445a276 100644
--- a/Performance/cyrk_performance-RK45.csv
+++ b/Performance/cyrk_performance-RK45.csv
@@ -25,3 +25,4 @@ CyRK Version, Run-on Date, cython (avg), cython (std),CySolver (avg),CySolver (s
 0.11.0, 08/11/2024 13:33:35, 1.4157, 0.1032, 0.0647, 0.0010, 0.2163, 0.0069, 13.2409, 0.2058, 0.5789, 0.0121, 1.9120, 0.1314, 0.8558, 0.0112, 0.0684, 0.0049, 0.1836, 0.0083, 8.6953, 0.4141, 0.5989, 0.0051, 1.5423, 0.0157, 1.6548, 0.0253, 0.0998, 0.0011, 0.3134, 0.0323, 24.1535, 0.2564, 1.4176, 0.0167, 4.1044, 0.1653, 2.0023, 0.0453, 0.1063, 0.0046, 0.5995, 0.0094, 29.3136, 0.4531, 1.4513, 0.0394, 8.1298, 0.0426
 0.11.2, 13/11/2024 09:46:20, 1.2497, 0.0190, 0.0645, 0.0006, 0.2333, 0.0074, 12.5791, 0.2532, 0.6023, 0.0184, 2.0329, 0.0377, 0.8824, 0.0160, 0.0676, 0.0001, 0.1932, 0.0074, 8.4859, 0.0106, 0.6328, 0.0072, 1.5491, 0.0287, 1.7246, 0.0188, 0.1056, 0.0016, 0.3201, 0.0090, 25.1528, 0.5846, 1.4855, 0.0071, 4.6009, 0.1266, 2.0235, 0.0174, 0.1135, 0.0025, 0.6057, 0.0057, 29.2690, 0.5148, 1.5547, 0.0225, 8.8452, 0.2573
 0.11.3, 18/11/2024 22:00:10, 1.1977, 0.0041, 0.0614, 0.0006, 0.2097, 0.0059, 11.8970, 0.1254, 0.5723, 0.0119, 1.9069, 0.0544, 0.8432, 0.0066, 0.0659, 0.0009, 0.1745, 0.0030, 8.4187, 0.3196, 0.6140, 0.0064, 1.7302, 0.1918, 1.7725, 0.0502, 0.1088, 0.0022, 0.3057, 0.0097, 24.8389, 1.0028, 1.6105, 0.0791, 4.4626, 0.1301, 2.0433, 0.0796, 0.1261, 0.0023, 0.6580, 0.0422, 29.5806, 1.1944, 1.6120, 0.0302, 9.2682, 0.4399
+0.11.4, 25/11/2024 13:47:34, 1.1965, 0.0067, 0.0656, 0.0007, 0.2081, 0.0011, 12.0259, 0.1258, 0.5992, 0.0031, 1.9689, 0.0350, 0.8615, 0.0301, 0.0710, 0.0015, 0.1781, 0.0037, 8.1765, 0.0930, 0.6150, 0.0051, 1.5196, 0.0210, 1.6505, 0.0132, 0.1175, 0.0006, 0.3060, 0.0093, 24.5149, 0.4404, 1.6367, 0.0394, 4.0881, 0.0509, 1.9450, 0.0223, 0.1238, 0.0014, 0.5835, 0.0187, 28.3889, 0.4499, 1.6710, 0.0108, 8.3209, 0.1614
diff --git a/README.md b/README.md
index 4418d20..dadad4e 100644
--- a/README.md
+++ b/README.md
@@ -11,7 +11,7 @@
 
 ---
 
-<a href="https://github.com/jrenaud90/CyRK/releases"><img src="https://img.shields.io/badge/CyRK-0.11.3 Alpha-orange" alt="CyRK Version 0.11.3 Alpha" /></a>
+<a href="https://github.com/jrenaud90/CyRK/releases"><img src="https://img.shields.io/badge/CyRK-0.11.4 Alpha-orange" alt="CyRK Version 0.11.4 Alpha" /></a>
 
 
 **Runge-Kutta ODE Integrator Implemented in Cython and Numba**
@@ -28,7 +28,7 @@ The [cython-based](https://cython.org/) `cysolver_ivp` function that works with
 An additional benefit of the two cython implementations is that they are pre-compiled. This avoids most of the start-up performance hit experienced by just-in-time compilers like numba.
 
 
-<img style="text-align: center" src="https://github.com/jrenaud90/CyRK/blob/main/Benchmarks/CyRK_SciPy_Compare_predprey_v0-11-3.png" alt="CyRK Performance Graphic" />
+<img style="text-align: center" src="https://github.com/jrenaud90/CyRK/blob/main/Benchmarks/CyRK_SciPy_Compare_predprey_v0-11-4.png" alt="CyRK Performance Graphic" />
 
 ## Installation
 
diff --git a/pyproject.toml b/pyproject.toml
index 8fd0758..9e840a2 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name='CyRK'
-version = '0.11.3'
+version = '0.11.4'
 description='Runge-Kutta ODE Integrator Implemented in Cython and Numba.'
 authors= [
     {name = 'Joe P. Renaud', email = 'joe.p.renaud@gmail.com'}


Test patch:
diff --git a/Tests/D_PySolver_Tests/test_a_pysolve_ivp.py b/Tests/D_PySolver_Tests/test_a_pysolve_ivp.py
index 01a15d4..17af923 100644
--- a/Tests/D_PySolver_Tests/test_a_pysolve_ivp.py
+++ b/Tests/D_PySolver_Tests/test_a_pysolve_ivp.py
@@ -232,21 +232,6 @@ def test_pysolve_ivp_errors():
                         args=None, rtol=rtol, atol=atol,
                         pass_dy_as_arg=False)
     
-    with pytest.raises(AttributeError) as e_info:
-        # Check for unsupported number of dependent variables
-        result = pysolve_ivp(diffeq_scipy_style, time_span, np.linspace(0.0, 1.0, 1000),
-                        method="RK23",
-                        args=None, rtol=rtol, atol=atol,
-                        pass_dy_as_arg=False)
-    
-    with pytest.raises(AttributeError) as e_info:
-        # Check for unsupported number of additional captures
-        result = pysolve_ivp(diffeq_scipy_style, time_span, initial_conds,
-                        method="RK23",
-                        args=None, rtol=rtol, atol=atol,
-                        num_extra=1000,
-                        pass_dy_as_arg=False)
-    
     with pytest.raises(AttributeError) as e_info:
         # Check for bad first step
         result = pysolve_ivp(diffeq_scipy_style, time_span, initial_conds,