Sunday, May 14, 2017

CUDA in Visual Studio 2017

Note: this article only shows how to compile Visual Studio 2015 CUDA projects in Visual Studio 2017. For actual VS2017 support we will have to wait for a new CUDA release.

I previously wrote a small article on CUDA support for VS2015, to support CUDA compilation of older projects. Following the same principle we can 'hack' CUDA compilation support in VS2017. 

What you need
  • CUDA installation with visual studio integration for VS. I used CUDA 8.0 and VS2015 respectively.
  • VS2017 (any edition)
Copying the required files
  • To allow CUDA compilation we have to copy a few files. Find the CUDA 8.0 setting files in the VS2015 buildcustomizations directory:
C:\Program Files (x86)\MSBuild\Microsoft.Cpp\v4.0\V140\BuildCustomizations
Note: If you use a different VS version, you have to change the 'V140' accordingly (V120 for VS2013 for example).
  • Copy the following files: CUDA 8.0.props, CUDA 8.0.targets, CUDA 8.0.xml, and Nvda.Build.CudaTasks.v8.0.dll
  • Find the VS2017 buildcustomizations directory:
C:\Program Files (x86)\Microsoft Visual Studio\2017\Community\Common7\IDE\VC\VCTargets\BuildCustomizations
Note: I used the VS2017 Community edition. If you have another edition, change 'Community' in the path accordingly.

  • Paste the CUDA files here.

That's it
You can now load and compile your VS2015 CUDA projects in VS2017. When you first open your project in VS2017, make sure to not upgrade your project to VS2017, otherwise this won't work. 

Friday, April 28, 2017


I wrote a simple C# code interface for ini files. There are already great NuGet packages for ini file IO parsing and writing, but not a lot of packages that automatically generate a code layer. For this project I based my solution on the ini parser to do the file IO.

My package only provides a small overlay to create a C# class which handles all the file IO behind the scenes. Using the text-templates, we define both the ini file and create a C# class. An example file I used to create my configuration:

<#@ include file="$(ProjectDir)" #>
    // All properties in the ini file
    // Name, default value and category
    CreateProperty("Width", 1280, "Video");
    CreateProperty("Height", 720, "Video");
    CreateProperty("Fullscreen", false, "Video");

    // Generate the code layer

Which will create a C# class with the same name as your text-template. The ini file will either be created the first time you use this class, or the old values will be read from the existing file.

Usually there is no backwards compatibility with older versions of the ini file. If you add new properties, all values in the ini file will be reset to their defaults. I avoid these scenarios using the beautiful functionality to merge two ini files from the ini parser. I can simply add the new properties to the old ini file without changing their values.

Finally, an example of the above template used in code:

// Use the namespace where you placed the template
using IniGenerator.Content.Generated;

// Name of ini file
var config = new Config();
// Can be used directly in code without parsing
var size = new Size(config.Width, config.Height);
var fullscreen = config.Fullscreen;

You can view the source code on GitHub, or download the package from NuGet.

If you have any feedback, leave a comment or post an issue on the GitHub project.

Saturday, April 22, 2017

Master Thesis

Update: You can download the full thesis here.

Level-of-Detail Independent Voxel-Based Surface Approximations was the subject of my master thesis. I wrote a small dissemination that explains the basics of my thesis on this page.

This image shows the final result of my thesis work. The models above are voxel models with 4096 (2^12) voxels in every axis. If they were all filled, I would have to store 4096^3 = 68719476736 voxels in total. There has been a lot of research into compressing the huge amount of data this requires, I mentioned some examples on the thesis page.

Using a Sparse Voxel Octree (SVO) storing scalar field values, the six models above can be stored in 12GB of memory total. Using my multiresolution method we can store visually comparable models in only 2GB of memory total.

Here is a small video showing the current state of the voxel path tracer:

Thursday, October 27, 2016

Master thesis current state

For my master thesis, I've been working on a project for some time now. In this post I'll share a few debug screenshots of the current state of the project.

The project can load triangle meshes of (almost) any format, and convert them to large resolution sparse voxel octrees (SVO). In the picture above, you can see the voxelized Lucy model, which is about 500MB in triangle format.

Converted to an SVO, storing only which voxels are on or off, we can store data for up to 4096^3 voxels in only ~40MB. That's a boolean for ~68 billion voxels. But since we only store voxels that are on, this number changes drastically, thus we are able to compress this pretty well.

If you want to store more data for every voxel, the amount of memory is going to skyrocket. In the example above, I store a scalar field for every voxel (that's 8 floating point numbers per voxel). All that data nets nearly 870MB for the highest resolution.

I was able to convert and draw a model up to 8192^3 resolution, including all simplifications of a dragon model:

Where you can see that the voxel resolution is even larger than the millions of triangles stored in the model.

For my master thesis I will be working on creating level of detail simplifications. The screenshots below show some results of a linear approximation calculated for a group of voxels:

The first model at 512^3 resolution, and the second at 2048^3.

These images are rendered using raymarching to find the intersection point of a ray and a scalar field. I wrote the raytracer in CUDA, which allows for an interactive framerate even with gigantic data sets by using parallelization on the GPU.

Finally, some more renders:

Wednesday, July 20, 2016

The next step in Entity Component Systems

Let's talk game engines. For a current project, I'm writing a game engine completely from scratch. Before I started I wrote down some key functionalities the engine should be able to handle. After writing that down, the proces of creating the engine boils down to optimizing the functionality in terms of performance and memory requirements.

Before we dive into engine compositions, let's look at two big AAA game engines used to create games which are used by a large number of developers today.

Unreal Engine 4 is mostly known because of the impressive graphics that can be created within a few mouseclicks. The engine is open source, and is an all-round engine that supports nearly everything you can think of when creating a game. Below you will find some amazing pictures rendered using Unreal Engine 4.

Complete album here.

Unity is an all-round game engine designed to ease the development process by working with C# as its core language. Unity is a lot more oriented towards an entity component system than Unreal Engine 4.

These engines took years to develop, and can practically support any use case. For my engine I'm looking to only support a very narrow subset of these use cases.

The thing both engines have in common is that they are both entity component systems, which is what the remainder of this post is all about.

Engine compositions
Mainly, there are two compositions for game engines. The first one is Object Oriented Programming (OOP), and the second type is an Entity Component System (ECS). There are already a lot of resources available to learn about both. I compiled a small list of examples, and will revisit the topic briefly.

[1] Understanding Component-Entity-Systems by Boreal Games
Shows a clear and concise introduction to OOP vs ECS systems.
[2] Implementation of a component-based entity system in modern C++ by Vittoreo Romeo for CPPCon
Explains the ECS system in depth and shows an implementation in C++
[3] Evolve your Hierarchy by Mick West
Experience from a programmer changing from OOP to an ECS system. Explains cache optimizations

Object Oriented Programming
The main reason to use OOP for games, was that the concept is easy to grasp. You create a hierarchy for all objects, and code reuse is introduced by polymorphism. In the diagram below you can see an example of OOP used in a game engine.

Image from [1]
It's clear to see that the EvilTree can't fit in the hierarchy, because it would require inheritance from both static and dynamic entities. While this is possible in some languages, it can lead to difficulties known as the diamond problem.

Entity Component System
The entity component system is designed to solve the problem stated above. The structure above would look as follows in an ECS:

Image from [1]
Where you can see that all the objects are derived from different components. The components can be reused for any object, this makes it easy to add new entities.

The next step for Entity Component Systems
Now that we are up to speed with game engine compositions, let's look at the path forward.

Data Oriented Design (DOD)
This paradigm is the design principle behind the ECS system. The basic principle is instead of looking at code, look at data. This concept is derived from the fact that most applications are memory bound instead of processing power.

The main point for DOD in game engines focusses on using the ECS in a way that avoids cache misses. To illustrate this, we look at the table below.

Image from [3]
In this ECS from [3], we can see on the left all the entities in the game, and in the table all of the components they exist of. When viewed from a higher scale, the only thing we changed is instead of looking at the diagram from left to right, we now look at it from top to bottom. So in our code we define our components as a list of Position components, Movement components, and so on.

For every entity we add, we simply add one of each component to all component arrays. This is why there are holes in the diagram.

All the way at the top of this article, I said that the engine is a balance between performance and memory. We can now clearly see why this is the case: this layout consumes a lot of memory to increase the performance. The "Script only" object creates 5 empty components which will never be used.

The three principles
We can define three points to weigh all our engine needs.
  1. Cache efficiency
  2. Memory efficiency
  3. Parallel efficiency

Now we have to define how to determine the efficiency of all of these points.
  1. Measuring cache efficiency is tricky at best, and this is very dependent on optimization. So instead of a ratio, we define the following: An engine becomes more efficient if we require fewer arrays available at the same time, and the smaller the size of a single item in the array the better
  2. The ratio for memory efficiency is defined as the total used components divided by the total created components.
  3. We can determine the parallel efficiency by looking at the percentage of work that can be executed in parallel. For a complete calculation of the speedup, we can look at Amdahl's law.

ECS revisited
Now that we have the three principles, we can discuss the efficiency of the ECS system. If we assume that we are using the ECS from [3]:
  1. We can see that this would lead to a good cache efficiency, but not the best: A physics component can't operate without a position component, this means we need multiple arrays simultaneously when updating entities. But the big advantage here is that all the arrays are separated, and can be queried individually per component.
  2. The memory efficiency is bad. The ratio for this particular example is already 19/30 = 63%. That means that 37% of our memory is just thrown away for the sake of efficiency.
  3. The parallel efficiency leads to a tricky scenario: how can you execute ECS in parallel? In this particular case, we pretty much can't. The physics system updates the position component, and only after that, we can render the component using the new position.

    In order to process everything in parallel, we'd have to look from left to right (per object) again, and processing the objects in parallel would require ALL data available in caches, which in turn would lead to a lot of cache misses.. 

In conclusion, we can say that we traded off space for time. We require more memory, but less processing time due to cache efficiency to process all our objects.

As for parallel efficiency, from a DOD composition such as this, we can run some tasks in parallel. If we look at the implementation from [2], every entity has a signature:

Image from [2]

In this case, the signature tells us that we require AI and Enemy components. In the implementation, we have systems that update all entities containing specific signatures. To create a simple parallel processing ECS, we can simply say that every set of systems that don't have any component in common can execute at the same time.

In our example, with a physics system and rendering system, this would not work, since they both rely on a position component.

A solution presented in Vittorio Romeo's presentation is to store all the components in one mega-array:

Image from [2]
While this could be the solution, I very much disliked the amount of work required to implement this, and keep up with all the overhead of adding and removing components.

Setting up the engine
In my engine I opted for an ideal solution, within the constraints of C++ (static types, known at compile time). With the power of C++ 11, we can reach a lot using variadic templates, and I built a lot of my implementation with it.

I based my system on the implementation of [2]. I use signatures to define a set of components, and I also define systems by providing a signature. But instead of storing components separately in one array per component type, I took a step back in 'engine progression'. I define one array for every possible signature.

If you read that carefully, you should already be thinking, that would require a lot of arrays! If we define up to 64 different components, and generate one array for every possible signature, we would have 1.8446744073e+19 arrays. Possibly requiring more memory than the complete application that we're building.

So instead of creating one array for all possible signatures, let's create one array for every signature used. The tricky part is finding all possible signatures at compile time. And we can't do that in C++, since we have no such library as reflection from C#, where we could query all that.

Text templates
We will use C# to create our signatures before the C++ compile time, so we know all the types at compile time. For this we will use something commonly used in web development: text templates. If it's your first time working with these in visual studio, I recommend the syntax highlight plugin.

So in text templates, we can write code that writes code, quite nifty. A small example that generates a list of all our components:

<#@ template debug="false" hostspecific="false" language="C#" #>
<#@ assembly name="$(TargetDir)CodeGeneratorFunctions.dll" #>
<#@ import namespace = "CodeGeneratorFunctions" #>
<#@ output extension=".h" #>
// This file was automatically generated
#pragma once
 string[] components = Generator.GetComponents();
 foreach(string component in components)
  WriteLine("#include \"Components\\" + component + ".h\"");

Where I define a function "GetComponents()" in a DLL CodeGeneratorFunctions, to search all our files for component signatures. I was pleasantly surprised with the runtime, as I thought it would take years to search all those files, but it actually took less than a couple of seconds.

Similar to this example, I wrote text templates for all signatures and systems.

The engine
Back to engine talk, now that we have our component composition, let's determine how efficient this engine could be in theory.

  1. Cache efficiency. At first glance, the cache efficiency would suffer in comparison with the original ECS from [3], but it's hard to tell. We have a larger single item size in the array, but we only require one array simultaneously. Below I explain a small optimization that reduces the individual item size. 
  2. Our memory efficiency is great. Since we can basically store only the necessary components for every object, we waste 0% of memory, with a little overhead of creating a lot of arrays in larger systems. 
  3. The parallel efficiency is similar to the ordinary ECS. We can only execute systems in parallel when they contain unique component sets. 
So in theory, we got an advantage in terms of memory. But we are still questioning if the cache efficiency changed. Looking back at the mega-array structure, we can see that we improved a little, we can store our velocity and position together in one array, so we don't have to acces two separate arrays and have cache misses. 

The main disadvantage of this structure is when you use larger objects. If we have an object storing a lot of components, the item size of the array is going to be large. And a large item size means cache misses. We can make our engine more cache friendly by using hot/cold data separation

As example, we consider objects storing data for physics (position, velocity) and rendering (huge models of 300MB). Loading only two of these objects, is a guaranteed cache miss, since the two objects are 300MB apart in the memory lane. The solution is to store a reference to the model, rather than storing it completely, and only call the model when it's required for rendering. This way we can update the physics without cache misses (a pointer is only 8 bytes). 

The hot/cold data separation separates hot data (used multiple times) versus cold data (used sparsely). If you want to read more, I suggest reading the article on gameprogrammingpatterns about this topic

To improve up on the parallel execution of the system, we can schedule all the work of one system in parallel. Since one system will be handling multiple arrays of objects, the first option is to execute all of these in parallel, and the second option is to execute all of the items in one array in parallel. Since the arrays have different lengths, the first option would be a bad choice (one thread would take very long, while the others would be waiting). Thus linearly processing all arrays, and execute all items inside that array is the best option. Further optimizations can be done by using SIMD instructions, but that is outside the scope for this article. 

Future work
I'd like to see if I can increase parallel efficiency by introducing tech from Naughty Dog's engine. They have a great presentation about it available online. Instead of introducing parallel execution per system, I'd like to see if we can create a set of instructions per signature and create a fiber to execute that.

When I'm done creating the engine (which is never, because it's a game engine) I will compile some benchmarks compared to a 'normal' ECS implementation. I also hope to release some source code from this engine.

[1] Understanding Component-Entity-Systems by Boreal Games
[2] Implementation of a component-based entity system in modern C++ by Vittoreo Romeo for CPPCon
[3] Evolve your Hierarchy by Mick West
[4] What is data oriented design StackOverflow
[5] Introduction to data oriented design DICE
[6] Gameprogrammingpatterns: Data Locality
[7] Parallelizing the Naughty Dog engine using fibers

Thursday, February 25, 2016

CUDA in Visual Studio 2015

Update September 2016: CUDA 8.0 is available, Visual studio update 3 is supported.
Update June 2016: CUDA 8.0 RC is available. Visual studio 2015 is supported, but update 2 is not yet included.

In this small post I will explain how you can use CUDA 7.5 in Visual Studio 2015. I don't claim to have full support for VS2015, merely using the VS2015 editor and compiling a VS2013 project. You will still need to have Visual Studio 2013 installed, with the CUDA toolkit extension.

On a separate note: Nsight does have support for VS2015, so no hacks required there!

What you need:

  • Any C++ project using CUDA.
  • CUDA 7.5 toolkit with VS2013 support installed (I only tested it with 7.5, but I can imagine other versions working as well)

In the project properties, make sure the project compiles for "v120", which is VS2013. 

To actually load the project in VS2015 having support for compiling CUDA code, we have to copy some files. Visual Studio uses targets to include several extensions for loading project files. We simply have to copy the VS2013 support to the VS2015 folder:

The CUDA 7.5 extension files for VS2013 are located in:
C:\Program Files (x86)\MSBuild\Microsoft.Cpp\v4.0\V120\BuildCustomizations

Find CUDA 7.5.props, CUDA 7.5.targets and CUDA 7.5.xml

If you simply copy them to the same folder for VS2015:
C:\Program Files (x86)\MSBuild\Microsoft.Cpp\v4.0\V140\BuildCustomizations

You can now load these projects in VS2015. 

My raytracer running from VS2015 using Nsight. 

Monday, December 21, 2015

Real-time Raytracing part 4

In all of the previous posts, I've talked about optimizing ray tracing. I've mentioned path tracing, but never spoke of path tracing specific optimizations. This post will be a small survey of implementations and research about path tracing optimizations.


Path tracing is great for simulating global illumination by tracing rays, and of course, these rays have to follow "realistic paths". As observed a long time ago (1760) by Johann Lambert, intensity of light from an ideal diffuse surface is directly proportional to the cosine of the angle $\theta$ between the direction of the incident light and the surface normal. This is called Lambert's cosine law in optics.

In path tracing, we mainly use the cosine law to calculate the contribution of light from the next ray. When tracing from a diffuse surface, the direction of the next ray is usually calculated by generating a random direction in the hemisphere of the surface. When tracing from a mirror, you can imagine there is only one direction on the hemisphere that is correct: the perfect reflection. Metal-like surfaces contain both elements, called glossy reflections. This image shows what I'm talking about in 2D:

If you would order them by calculation cost for the path tracing algorithm, the reflection would be the easiest, there is only one solution, one ray to calculate. The glossy one is harder to calculate, since you have to accumulate rays with different weights, but still simpler to fully calculate than the diffuse option. Besides these simple examples, materials can contain many functions to bend incoming rays. The collection of these functions describe the Bidirectional Reflectance Distribution Function (BRDF). Which in turn, you can even extend to allow for rays extending through the surface with a Bidirectional Scattering Distribution Function (BSDF).

Cosine weighted sampling
The intensity of light from an ideal diffuse surface is directly proportional to the cosine of the angle $\theta$ between the direction of the incident light and the surface normal.
This leads to an interesting optimization: cosine weighted sampling. Since the incoming and outgoing light is measured by a cosine, the outgoing rays nearly parallel to the surface don't add any (useful) information to the final solution. I could write this whole post about this optimization, but that would be a waste of time, since there are already many good examples out there. This post from Rory Driscoll shows results as well as the maths behind it.

This image shows the cosine weighted sampling on the top and the uniform sampling on the bottom. This was taken from the pathtracing blog
Sampling optimization

As mentioned above, when you've found a ray-triangle collision, the direction of the new ray is usually 'bruteforce' calculated by taking a random direction on the hemisphere. For diffuse surfaces, this is no problem, since every outgoing direction weights the same. If you would apply the same to perfectly reflecting surfaces, chances are you will most likely never find the correct outgoing ray.

By knowing the material properties you can determine how to sample the outgoing ray. This sampling strategy can be a very easy optimization if you're willing to end up with an approximation. If you look at the 3 examples from the image above: one variable for the degree of reflectiveness can produce these examples.

The problems with this reflectiveness variable for path tracing are the weights shown in the glossy example. Solving the Monte Carlo problem of path tracing requires a probability distribution function (PDF) over these samples. The outer rays in the glossy examples are shown smaller, because they affect the outcome less than the rays from the perfect reflection.

This optimization is as simple or as hard as you want to make it. The simpler you keep the sampling, the more approximate your solution is going to be. The best solution would be to sample your chosen BRDF. The cosine weighted sampling is shown to be perfect for diffuse only materials (diffuse materials have perfect lambertian reflectance). I found a sampling method for the Cook-Torrance BRDF in this paper: Microfacet Models for Refraction through Rough Surfaces.

Direct and indirect lighting

Direct lighting is easy to calculate. This is shown in almost all released 3D games out there. Simply calculating a dot product of the surface normal and light direction would result in perfect lambertian shading.

In path tracing we can apply an optimization to allow for direct light sampling. It's called next event estimation. This is where the math and statistics get nasty. To allow for direct light sampling, we have to apply a different probability distribution function. We have to sample the light and divide the contribution of direct light by the area of the light:
Area sampling. Image taken from [2]

Whereas the indirect illumination should be sampled by the original method using hemispheres. With one exception, all of the rays hitting the light should be discarded (since the light gets sampled by the other PDF from above).
Hemisphere sampling without direct light sampling (dashed lines). Image taken from [2]
These examples show only sampling of a single light, which is easy to maintain. Imagine a scene with thousands of lights and one sun. If you apply the same theory of the two PDFs from above, you would end up sampling more inefficient than the brute force sampling. The solution is rather simple, we add weights to each light. But these have to be based on different criteria: only adding weights based on distance could influence the effects of the sun (large distance, huge brightness) versus normal lights (small distance, small brightness). Other small optimizations could be added based on needs: you don't have to continually sample the sun during night time and so on. If you like to know the mathematics and details behind these two PDFs, I suggest you read the presentations from the references below.

Multiple importance sampling

In larger applications, thousands of material types and BRDFs are going to be used. Having a different sampling strategy for each of them is going to be time consuming and frankly undo-able. We already have two different PDFs to sample all of these materials, the direct and indirect sampling. The problem that arises from all of this shown in images:

These are the two sampling methods shown from above, the BSDF sampling is the hemisphere indirect sampling and the light source sampling is the direct sampling using the area of the light. The problem is the glossy reflection on the four plates. You can see that indirect sampling works if the light is large enough to have some collisions with rays, and the direct light source sampling works fine for small area lights.

Multiple Importance Sampling is a technique that can combine different sampling techniques to find a low variance estimate of the integral. By combining both sampling methods we can get the following result:
Images taken from [4]

Bidirectional path tracing

This leads us to more recent techniques in path tracing, namely bidirectional path tracing. This technique is based on the inverse of path tracing, instead of finding the light by tracing rays from the camera, find the camera by tracing from the light. These are the shooting rays, and the original camera rays the gathering rays. The bidirectional part indicates that these two types of rays can be combined.

This technique wasn't meant for real time path tracing applications which require responsiveness. Since for every gathering ray you shoot, the amount of calculation is doubled by also tracing shooting rays. This nearly doubles the time per frame, and thus decreases responsiveness. While you lose responsiveness, this algorithm increases the convergence rate for path tracing a lot, so in the end it's worth it.

As extension to bidirectional path tracing, there is an algorithm called Metropolis light transport. This algorithm allows finding more difficult light paths by extending existing paths. An example: A shooting and gathering ray have been found by the bidirectional raytracing method. Every bounce in these rays is recorded as a node. The metropolis light transport can add extra nodes on these rays to modify the existing ray to a new one. These nodes are placed in areas with high gradients. You can find more on this technique in this paper: Metropolis Light Transport.

Another extension I found, but have not fully explored is: Light Transport Simulation with Vertex Connection and Merging. The basic idea is to reuse existing paths by connecting vertices. This can increase the amount of generated paths per pixel:

Vertex merging: reusing paths to increase paths per pixel. Image taken from [6]
There is an opensource implementation of VCM called smallVCM. This implementation also contains multiple importance sampling and normal bidirectional path tracing.

Further reading

Most recent techniques include gradient domain tracing. It first originated from metropolis light transport in: Gradient-domain Metropolis Light Transport. The paper on Gradient Domain Bidirectional Path Tracing applies this to a bidirectional path tracer, and Gradient-Domain Path Tracing applies the same principle for normal path tracers. The path tracer also contains source code. You can see the path tracing algorithm is converging faster already with only one sample per pixel:

Showing the difference between Gradient domain path tracer (G-PT) and normal path tracer (PT). Image taken from [9].
Other blog posts:
Path tracing 3D fractals. Also explains cosine weighted sampling, importance sampling, next event estimation, and image based lighting. Implementation in GLSL.
Everything about ray tracing, and is currently working on a GPU path tracer.
Community for path- and ray tracing.





[3] Walter, Bruce, et al. "Microfacet models for refraction through rough surfaces."Proceedings of the 18th Eurographics conference on Rendering Techniques. Eurographics Association, 2007.
[4] Veach, Eric. Robust monte carlo methods for light transport simulation. Diss. Stanford University, 1997.
[5] Veach, Eric, and Leonidas J. Guibas. "Metropolis light transport." Proceedings of the 24th annual conference on Computer graphics and interactive techniques. ACM Press/Addison-Wesley Publishing Co., 1997.
[6] Georgiev, Iliyan, et al. "Light transport simulation with vertex connection and merging." ACM Trans. Graph. 31.6 (2012): 192.
[7] Lehtinen, Jaakko, et al. "Gradient-domain metropolis light transport." ACM Transactions on Graphics (TOG) 32.4 (2013): 95.
[8] Manzi, Marco, et al. "Gradient-Domain Bidirectional Path Tracing." (2015).
[9] Kettunen, Markus, et al. "Gradient-domain path tracing." ACM Transactions on Graphics (TOG) 34.4 (2015): 123.