diff --git a/assignments/shell-slurm-practice.md b/assignments/shell-slurm-practice.md
index 54e5b8a..460f0ea 100644
--- a/assignments/shell-slurm-practice.md
+++ b/assignments/shell-slurm-practice.md
@@ -24,6 +24,8 @@ The second script, `analyze.sh`, will be a bash script that gets submitted as a
 
 The third script, `submit.sh`, will be run on the login nodes (with `bash`, not `sbatch`) and submit a job array of size 10 that runs `energize.sh`, once per array task. It will then parse the job id from running sbatch (search the `sbatch` man page for "parsable") and submit a second job that is dependent on the first completing successfully; if the first job fails, the second job should not run. The second job will run `analyze.sh`. You will need to pass the job id from the first job to the `analyze.sh` either as a command line argument or an environment variable.
 
+**Make sure not to run `submit.sh` from a node-local directory like `/tmp` or `/dev/shm`**--if you do so, no outfile will show up since the job will try to write it to the compute node's storage rather than the storage of the node you're on.
+
 Put these scripts into a tar file. Compress the tar file using gzip. Submit this `.tar.gz` file through [Canvas](https://byu.instructure.com/courses/21221/assignments).
 
 ## Grading
diff --git a/readings/jthread.md b/readings/jthread.md
new file mode 100644
index 0000000..433bcbb
--- /dev/null
+++ b/readings/jthread.md
@@ -0,0 +1,155 @@
+# C++ Threading
+
+[`std::jthread`](https://en.cppreference.com/w/cpp/thread/jthread)s are C++ 20's flagship mechanism for [threading](threading.md). Unlike [OpenMP](openmp.md), adding them to a program means that the program is squarely parallel and must be compiled as such.
+
+
+
+## Compiling with C++ Threads
+
+`g++ -pthread ...` will compile with C++ threads; it's usually easy to find the required flag on the man page or the internet for any compiler. `CMakeLists.txt` requires two things: finding the threading package and linking it to a target:
+
+```cmake
+# At the top, under the `project` call:
+find_package(Threads REQUIRED)
+# With other compilation calls
+add_executable(blah blah.cpp)
+target_link_libraries(blah PRIVATE Threads::Threads)
+```
+
+
+
+## Using C++ Threads
+
+`std::jthread`s are simple in principle--they spawn, run a function while the main program is executing, then [join](https://en.cppreference.com/w/cpp/thread/jthread/join) automatically. For example, the following program prints arabic numerals in the main thread, latin in the other:
+
+```c++
+#include <thread>
+#include <chrono>
+#include <iostream>
+
+int main() {
+    // Latin numerals print in a spawned thread
+    auto latin_numeral_thread = std::jthread([]{ // using lambdas to spawn threads is the convention.
+        for (auto &numeral: {"I", "II", "III", "IV", "V"}) {
+            std::this_thread::sleep_for(std::chrono::milliseconds(50));
+            std::cout << numeral << std::endl;
+        }
+    });
+    // Arabic numerals print in the main thread
+    for (int i=1; i<=5; i++) {
+        std::this_thread::sleep_for(std::chrono::milliseconds(45));
+        std::cout << i << " is written as ";
+    }
+    return 0; // latin_numeral_thread is automatically joined
+}
+
+/* Compile with `g++ -std=c++20 -pthread -o numerals numerals.cpp`
+ * Output looks like:
+ * 1 is written as I
+ * 2 is written as II
+ * 3 is written as III
+ * 4 is written as IV
+ * 5 is written as V
+ */
+```
+
+This is simple enough when no coordination is required, or when clock-based coordination is sufficient as above, but getting threads to synchronize or communicate correctly is challenging. Even getting two threads to interleave the printing of "PING" and "PONG" requires 3 variables dedicated solely to coordination:
+
+```c++
+#include <iostream>
+#include <thread>
+#include <mutex>
+#include <condition_variable>
+
+int main() {
+    // Simulation parameters
+    const int n_volleys = 4;
+    bool pinging = true; // start with PING
+    std::mutex mtx;
+    std::condition_variable cv;
+    // PONG in worker thread
+    auto pong_thread = std::jthread([&]{
+        for (int i=0; i<n_volleys; i++) {
+            std::unique_lock lock(mtx);
+            cv.wait(lock, [&]{ return !pinging; });
+            std::cout << "PONG!" << std::endl;
+            pinging = !pinging;
+            cv.notify_one();
+        }
+    });
+    // PING in main thread
+    for (int i=0; i<n_volleys; i++) {
+        std::unique_lock lock(mtx);
+        cv.wait(lock, [&]{ return pinging; });
+        std::cout << "PING, ";
+        pinging = !pinging;
+        cv.notify_one();
+    }
+    return 0;
+}
+
+/* Output:
+ * PING, PONG!
+ * PING, PONG!
+ * PING, PONG!
+ * PING, PONG!
+ */
+```
+
+
+
+## Thread Coordination
+
+### C++ Atomics
+
+[Atomic operations](https://www.cplusplus.com/reference/atomic/atomic/) appear to happen instantaneously from the perspective of threads--multiple threads can write to [atomics](https://www.cplusplus.com/reference/atomic/atomic/) without worrying about [race conditions](threading.md#race-conditions-atomic-operations-and-reductions). The tradeoff is a severe performance penalty if you use them heavily--the following example code will do the same thing as [the reduction example code](openmp.md#reductions) and use 20x the CPU time:
+
+```c++
+#include <iostream>
+#include <atomic>
+
+int main() {
+    std::atomic<size_t> counter = 0;
+    #pragma omp parallel for
+    for (size_t i = 0; i < 20000000; ++i) {
+       counter += 1;
+    }
+    std::cout << counter << std::endl;
+    return 0;
+}
+```
+
+### C++ Mutexes
+
+[`std::mutex`](https://en.cppreference.com/w/cpp/thread/mutex) and [`std::counting_semaphore`](https://en.cppreference.com/w/cpp/thread/counting_semaphore) provide [mutexes and sempahores](threading.md#mutexes-and-semaphores) in C++. It's best to [use](#using-c-threads) [`std::unique_lock`](https://en.cppreference.com/w/cpp/thread/unique_lock) and [`std::lock_guard`](https://en.cppreference.com/w/cpp/thread/lock_guard) rather than locking and unlocking mutexes manually.
+
+### C++ Condition Variables
+
+[Condition variables](https://www.cplusplus.com/reference/condition_variable/condition_variable/) encapsulate the idea of waiting until some condition is true. They have three main methods:
+
+- `wait`
+- `notify_one`
+- `notify_all`
+
+The example below shows a simple thread-safe queue with `push` and `pop` methods. Safety is ensured by the `condition_variable` and `mutex`. **Take a bit of time to make sure you understand how this works and why it's safe**:
+
+```c++
+std::mutex mtx;
+std::condition_variable cv;
+auto pop(std::queue &queue) {
+   std::unique_lock lock(mtx);
+   while (queue.empty()) {
+      cv.wait(lock);
+   }
+   return queue.pop();
+}
+void push(std::queue &queue, auto val) {
+   std::unique_lock lock(mtx);
+   queue.push(val);
+   cv.notify_one();
+}
+```
+
+### C++ Barriers
+
+Use [`std::barrier`](https://en.cppreference.com/w/cpp/thread/barrier) for [barriers](threading.md#barriers) in C++20.
diff --git a/readings/openmp.md b/readings/openmp.md
index fcdd318..ea1331b 100644
--- a/readings/openmp.md
+++ b/readings/openmp.md
@@ -73,8 +73,15 @@ OpenMP comes with these built-in reduction operators in C and C++:
 The race condition in the [first example](#openmp-threading), which results from multiple threads trying to simultaneously modify `counter`, can be fixed with a sum reduction:
 
 ```c++
-#pragma omp parallel for reduction(+:counter)
-for (long i{0}; i < 20'000'000l; ++i) {
-   counter += 1;
+#include <iostream>
+
+int main() {
+    size_t counter = 0;
+    #pragma omp parallel for reduction(+:counter)
+    for (size_t i = 0; i < 20000000; ++i) {
+       counter += 1;
+    }
+    std::cout << counter << std::endl;
+    return 0;
 }
 ```